# Probabilistic gradient pruning

Tutorial Author: Zirui Li, Hanrui Wang

bugs:
 - Directly run all. In the first epoch, the process will get stuck in the inference phase.
 - modify `torchquantum/plugins/qiskit_processor.py:228 parallel=True` to `parallel=False` to fix the bug.

## Outline
 - Introduction to probabilistic gradient pruning.
 - Train a model with gradient pruning.

## Introduction to probabilistic gradient pruning

By carefully investigating the on-chip training process, we observe that small gradients tend to have large relative variations
or even wrong directions under quantum noises. Also, not all gradient computations are necessary for the
training process, especially for small-magnitude gradients.

The observations provide great opportunities for us to boost the robustness and efficiency of QNN on-chip learning. Inspired by that, we
propose a **probabilistic gradient pruning** method to predict and only
compute gradients of high reliability. Hence we can reduce noise impact and also save the required number of circuit runs on real
quantum machines.

### Accumulation Window and Pruning Window

We separate all the training epochs into a repeat of an accumulation window followed by a pruning window. There are three important hyper-parameters in our probabilistic gradient pruning method:
  - accumulation window width 𝑤𝑎,
  - pruning ratio 𝑟,
  - pruning window width 𝑤𝑝 .

In the accumulation window, we collect the information of gradients in each training step. In each step of the pruning window, we probabilistically exempt the calculations of some gradients based on the information collected from the accumulation window and pruning ratio.

The accumulation window width
and pruning window width decide the reliability of the gradient
trend evaluation and our confidence in it, respectively. The pruning
ratio can be tuned to balance the gradient variances caused by noise
perturbation and pruning. Thus, the percentage of the time saved
by our probabilistic gradient pruning method is $𝑟\frac{𝑤𝑝}{𝑤𝑎+𝑤𝑝}× 100%$.
In
our experiments, we find that the setting (𝑤𝑎=1, 𝑤𝑝=2∼3, 𝑟=0.3∼0.5)
usually works well in all cases

##Train a model with probabilistic gradient pruning

###Installation

```python
!pip install qiskit==0.32.1
```

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```python
!git clone https://github.com/zhijian-liu/torchpack.git
```

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```python
%cd torchpack
```

    /content/torchpack

```python
!pip install .
```

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```python
%cd ..
```

    /content

Download and cd to the repo.

```python
!git clone https://github.com/mit-han-lab/torchquantum.git
```

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```python
%cd torchquantum
```

    /content/torchquantum

Install torch-quantum.

```python
!pip install --editable .
```

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    Installing collected packages: ppft, pox, fonttools, pathos, matplotlib, torchquantum
      Attempting uninstall: matplotlib
        Found existing installation: matplotlib 3.2.2
        Uninstalling matplotlib-3.2.2:
          Successfully uninstalled matplotlib-3.2.2
      Running setup.py develop for torchquantum
    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
    albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.
    Successfully installed fonttools-4.31.2 matplotlib-3.5.1 pathos-0.2.8 pox-0.3.0 ppft-1.6.6.4 torchquantum-0.1.0

Change PYTHONPATH and install other packages.

```python
%env PYTHONPATH=.
```

    env: PYTHONPATH=.

Run the following code to store a qiskit token. You can replace it with your own token from your IBMQ account if you like.

```python
from qiskit import IBMQ
IBMQ.save_account('0238b0afc0dc515fe7987b02706791d1719cb89b68befedc125eded0607e6e9e9f26d3eed482f66fdc45fdfceca3aab2edb9519d96b39e9c78040194b86e7858', overwrite=True)
```

```python
!pip install matplotlib==3.1.3
```

    Collecting matplotlib==3.1.3
      Downloading matplotlib-3.1.3-cp37-cp37m-manylinux1_x86_64.whl (13.1 MB)
         |████████████████████████████████| 13.1 MB 5.1 MB/s
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    Installing collected packages: matplotlib
      Attempting uninstall: matplotlib
        Found existing installation: matplotlib 3.5.1
        Uninstalling matplotlib-3.5.1:
          Successfully uninstalled matplotlib-3.5.1
    ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
    torchquantum 0.1.0 requires matplotlib>=3.3.2, but you have matplotlib 3.1.3 which is incompatible.
    albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.
    Successfully installed matplotlib-3.1.3

```python
!ls artifact
```

    aerbackend.py  example2  example4  example6  README.md
    example1       example3  example5  example7

```python
!cp artifact/aerbackend.py ../../usr/local/lib/python3.7/dist-packages/qiskit/providers/aer/backends/ -r
```

### Import modules

```python
import argparse
import os
import sys
import pdb
import json
import numpy as np
import torch
import torch.backends.cudnn
import torch.cuda
import torch.nn
import torch.utils.data
import torchquantum as tq

from torch.optim.lr_scheduler import CosineAnnealingLR

from torchquantum.datasets import MNIST
from examples.gradient_pruning.q_models import *
from torchpack.callbacks import (InferenceRunner, MeanAbsoluteError,
                                 MaxSaver, MinSaver,
                                 Saver, SaverRestore, CategoricalAccuracy)
from examples.gradient_pruning.callbacks import LegalInferenceRunner, SubnetInferenceRunner, \
    NLLError, TrainerRestore, AddNoiseInferenceRunner, GradRestore

# from torchpack import distributed as dist
from torchpack.environ import set_run_dir
from torchpack.utils.config import configs
from torchpack.utils.logging import logger

```

     WARNING: The qiskit parameterization bug is not fixed!

    run python fix_qiskit_parameterization.py to fix it!

###Function to build the callbacks

```python
def get_subcallbacks(config):
    subcallbacks = []
    for subcallback in config:
        if subcallback['metrics'] == 'CategoricalAccuracy':
            subcallbacks.append(
                CategoricalAccuracy(name=subcallback['name'])
            )
        elif subcallback['metrics'] == 'MeanAbsoluteError':
            subcallbacks.append(
                MeanAbsoluteError(name=subcallback['name'])
            )
        elif subcallback['metrics'] == 'NLLError':
            subcallbacks.append(
                NLLError(name=subcallback['name'])
            )
        else:
            raise NotImplementedError(subcallback['metrics'])
    return subcallbacks

def make_callbacks(dataflow):
    callbacks = []
    for config in configs['callbacks']:
        if config['callback'] == 'InferenceRunner':
            callback = InferenceRunner(
                dataflow=dataflow[config['split']],
                callbacks=get_subcallbacks(config['subcallbacks'])
            )
        elif config['callback'] == 'LegalInferenceRunner':
            callback = LegalInferenceRunner(
                dataflow=dataflow[config['split']],
                callbacks=get_subcallbacks(config['subcallbacks'])
            )
        elif config['callback'] == 'SubnetInferenceRunner':
            callback = SubnetInferenceRunner(
                dataflow=dataflow[config['split']],
                callbacks=get_subcallbacks(config['subcallbacks']),
                subnet=config['subnet']
            )
        elif config['callback'] == 'AddNoiseInferenceRunner':
            callback = AddNoiseInferenceRunner(
                dataflow=dataflow[config['split']],
                callbacks=get_subcallbacks(config['subcallbacks']),
                noise_total_prob=config['noise_total_prob']
            )
        elif config['callback'] == 'SaverRestore':
            callback = SaverRestore()
        elif config['callback'] == 'Saver':
            callback = Saver(max_to_keep=config['max_to_keep'])
        elif config['callback'] == 'MaxSaver':
            callback = MaxSaver(config['name'])
        elif config['callback'] == 'MinSaver':
            callback = MinSaver(config['name'])
        elif config['callback'] == 'GradRestore':
            callback = GradRestore()
        else:
            raise NotImplementedError(config['callback'])
        callbacks.append(callback)

    return callbacks

```

###Load configs
The config file describes everything about the model structure and the hyper-parameters of the training process, including batch size, learning rate, number of epochs, and whether to use qiskit's processor(we use qiskit's noise processor to train and test our quantum circuit in the following example).

```python
configs.load('examples/gradient_pruning/configs.yml')
if configs.debug.set_seed:
    torch.manual_seed(configs.debug.seed)
    np.random.seed(configs.debug.seed)
```

###Function to train
In this function, we create the dataset and the model according to configs. And we train our quantum model using qiskit's noise processor. The function will return a list of model accuracy after each epoch with respect to the number of inferences.

```python
def train_with_configs(configs):
  if os.path.exists("runs/probabilistic_gradient_pruning/summary/scalars.jsonl"):
    os.remove("runs/probabilistic_gradient_pruning/summary/scalars.jsonl")
  else:
    print("runs/probabilistic_gradient_pruning/summary/scalars.jsonl does not exist")

  device = torch.device('cuda')
  if isinstance(configs.optimizer.lr, str):
    configs.optimizer.lr = eval(configs.optimizer.lr)
  dataset = MNIST(
      root='./mnist_data',
      train_valid_split_ratio=[0.9, 0.1],
      digits_of_interest=[0, 1, 2, 3],
      n_test_samples=30,
      n_train_samples=50,
      n_valid_samples=30,
  )
  dataflow = dict()
  for split in dataset:
      sampler = torch.utils.data.RandomSampler(dataset[split])
      dataflow[split] = torch.utils.data.DataLoader(
          dataset[split],
          batch_size=configs.run.bsz,
          sampler=sampler,
          num_workers=configs.run.workers_per_gpu,
          pin_memory=True)

  model = QMultiFCModel0(configs.model.arch)

  if configs.qiskit.use_qiskit_train or configs.qiskit.use_qiskit_valid:
      from torchquantum.plugins import QiskitProcessor
      processor = QiskitProcessor(use_real_qc=configs.qiskit.use_real_qc, n_shots=configs.qiskit.n_shots, backend_name=configs.qiskit.backend_name)
      model.set_qiskit_processor(processor)

  model.to(device)

  total_params = sum(p.numel() for p in model.parameters())
  logger.info(f'Model Size: {total_params}')

  criterion = torch.nn.NLLLoss()
  optimizer = torch.optim.Adam(
      model.parameters(),
      lr=configs.optimizer.lr,
      weight_decay=configs.optimizer.weight_decay)
  scheduler = CosineAnnealingLR(optimizer, T_max=configs.run.n_epochs)

  from examples.gradient_pruning.trainers import ParamsShiftTrainer
  trainer = ParamsShiftTrainer(model=model,
                    criterion=criterion,
                    optimizer=optimizer,
                    scheduler=scheduler)

  trainer.set_use_qiskit(configs)
  run_dir = 'runs/probabilistic_gradient_pruning/'
  set_run_dir(run_dir)

  logger.info(' '.join([sys.executable] + sys.argv))

  logger.info(f'Training started: "{run_dir}".' + '\n' +
      f'{configs}')

  callbacks = make_callbacks(dataflow)

  trainer.train_with_defaults(
      dataflow['train'],
      num_epochs=configs.run.n_epochs,
      callbacks=callbacks)

  num_forward = []
  accu = []
  with open('runs/probabilistic_gradient_pruning/summary/scalars.jsonl', 'r') as json_file:
      json_list = list(json_file)

  for json_str in json_list:
      result = json.loads(json_str)
      if 'acc/test' in result.keys():
        num_forward.append(result['global_step'])
        accu.append(result['acc/test'])

  return num_forward, accu
```

```python
num_forward1, accu1 = train_with_configs(configs)
```

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    Extracting ./mnist_data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./mnist_data/MNIST/raw

    [2022-03-23 12:51:09.924] Only use the front 500 images as TRAIN set.
    [2022-03-23 12:51:09.983] Only use the front 30 images as VALID set.
    [2022-03-23 12:51:09.996] Only use the front 300 images as TEST set.

    /usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
      cpuset_checked))

    [2022-03-23 12:51:12.594] Cannot use MIT backend, roll back to open
    [2022-03-23 12:51:22.494] Model Size: 36
    [2022-03-23 12:51:22.554] /usr/bin/python3 /usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py -f /root/.local/share/jupyter/runtime/kernel-16eed591-b4a6-4540-88ba-fa352954d776.json
    [2022-03-23 12:51:22.558] Training started: "runs/probabilistic_gradient_pruning/".
    model:
      arch:
        n_nodes: 1
        n_wires: 4
        node_archs: [{'n_wires': 4, 'encoder_op_list_name': '4x4_ryzxy', 'n_blocks': 3, 'n_layers_per_block': 3, 'q_layer_name': 'barren_0'}]
        down_sample_kernel_size: 6
        act_norm: None
        pruning_method: gradient_based_pruning
        accumulation_window_size: 1
        pruning_window_size: 2
        pruning_ratio: 0.5
      name: q_multifc0
      transpile_before_run: False
      load_op_list: False
    dataset:
      input_name: image
      target_name: digit
    optimizer:
      name: adam
      lr: 0.05
      weight_decay: 0.0001
      lambda_lr: 0.01
    run:
      n_epochs: 15
      bsz: 25
      workers_per_gpu: 8
      device: gpu
    debug:
      pdb: False
      set_seed: True
      seed: 42
    callbacks: [{'callback': 'InferenceRunner', 'split': 'valid', 'subcallbacks': [{'metrics': 'CategoricalAccuracy', 'name': 'acc/valid'}, {'metrics': 'NLLError', 'name': 'loss/valid'}]}, {'callback': 'InferenceRunner', 'split': 'test', 'subcallbacks': [{'metrics': 'CategoricalAccuracy', 'name': 'acc/test'}, {'metrics': 'NLLError', 'name': 'loss/test'}]}, {'callback': 'MaxSaver', 'name': 'acc/valid'}, {'callback': 'Saver', 'max_to_keep': 10}]
    qiskit:
      use_qiskit: True
      use_real_qc: False
      use_qiskit_train: True
      use_qiskit_valid: True
      backend_name: None
      noise_model_name: None
      basis_gates_name: None
      n_shots: 8192
      initial_layout: None
      seed_transpiler: 42
      seed_simulator: 42
      optimization_level: 0
      est_success_rate: False
      max_jobs: 1
    [2022-03-23 12:51:25.368] Epoch 1/15 started.
      0%|          | 0/20 [00:00<?, ?it/s]

    /usr/local/lib/python3.7/dist-packages/qiskit/circuit/quantumcircuit.py:933: DeprecationWarning: The QuantumCircuit.combine() method is being deprecated. Use the compose() method which is more flexible w.r.t circuit register compatibility.
      return self.combine(rhs)

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    [loss] = 1.23, [lr/lr_group0] = 0.05, [nll_loss] = 1.23: 100%|██████████| 20/20 [13:28<00:00, 40.44s/it]
    [2022-03-23 13:04:54.195] Training finished in 13 minutes 28 seconds.
      0%|          | 0/2 [00:00<?, ?it/s]

    /usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
      cpuset_checked))

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    [loss] = 1.12, [lr/lr_group0] = 0.0478, [nll_loss] = 1.12: 100%|██████████| 20/20 [13:06<00:00, 39.33s/it]
    [2022-03-23 13:32:07.545] Training finished in 13 minutes 6 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 13:32:07.906] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 13:32:07.998] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.91s/it][2022-03-23 13:32:09.479] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 13:32:09.538] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.66s/it]
    [2022-03-23 13:32:10.876] Inference finished in 3.33 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 13:32:11.318] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 13:32:11.655] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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      8%|▊         | 1/12 [00:02<00:24,  2.21s/it][2022-03-23 13:32:13.117] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 13:32:13.193] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     17%|█▋        | 2/12 [00:03<00:18,  1.82s/it][2022-03-23 13:32:14.655] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 13:32:14.728] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     25%|██▌       | 3/12 [00:04<00:13,  1.50s/it][2022-03-23 13:32:15.784] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 13:32:15.846] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     33%|███▎      | 4/12 [00:06<00:12,  1.51s/it][2022-03-23 13:32:17.307] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 13:32:17.367] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     42%|████▏     | 5/12 [00:07<00:09,  1.37s/it][2022-03-23 13:32:18.419] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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    [2022-03-23 13:32:24.940] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 13:32:26.076] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:16<00:00,  1.37s/it]
    [2022-03-23 13:32:27.380] Inference finished in 16.5 seconds.
    [2022-03-23 13:32:27.394] No q_layer_op_list found, will not save it.
    [2022-03-23 13:32:27.407] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-2940.pt".
    [2022-03-23 13:32:27.408]
    + [acc/test] = 61
    + [acc/valid] = 56.667
    + [acc/valid/max] = 76.667
    + [loss] = 1.1162
    + [loss/test] = 1.0443
    + [loss/valid] = 1.1153
    + [lr/lr_group0] = 0.047839
    + [nll_loss] = 1.1162
    [2022-03-23 13:32:27.410] Estimated time left: 2 hours 44 minutes 8 seconds.
    [2022-03-23 13:32:27.418] Epoch finished in 13 minutes 26 seconds.
    [2022-03-23 13:32:27.420] Epoch 4/15 started.
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    [loss] = 1.02, [lr/lr_group0] = 0.0452, [nll_loss] = 1.02: 100%|██████████| 20/20 [14:01<00:00, 42.07s/it]
    [2022-03-23 13:46:28.859] Training finished in 14 minutes 1 second.
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    [2022-03-23 13:46:29.301] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 13:46:30.840] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.63s/it]
    [2022-03-23 13:46:32.141] Inference finished in 3.28 seconds.
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     25%|██▌       | 3/12 [00:05<00:15,  1.69s/it][2022-03-23 13:46:37.453] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:09<00:09,  1.57s/it][2022-03-23 13:46:42.044] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:11<00:07,  1.56s/it][2022-03-23 13:46:43.594] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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    [2022-03-23 13:46:46.738] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 13:46:48.266] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 13:46:49.798] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:19<00:00,  1.63s/it]
    [2022-03-23 13:46:51.669] Inference finished in 19.5 seconds.
    [2022-03-23 13:46:51.677] No q_layer_op_list found, will not save it.
    [2022-03-23 13:46:51.701] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-3932.pt".
    [2022-03-23 13:46:51.702]
    + [acc/test] = 65
    + [acc/valid] = 63.333
    + [acc/valid/max] = 76.667
    + [loss] = 1.0202
    + [loss/test] = 1.0286
    + [loss/valid] = 1.0648
    + [lr/lr_group0] = 0.045225
    + [nll_loss] = 1.0202
    [2022-03-23 13:46:51.704] Estimated time left: 2 hours 32 minutes 27 seconds.
    [2022-03-23 13:46:51.706] Epoch finished in 14 minutes 24 seconds.
    [2022-03-23 13:46:51.709] Epoch 5/15 started.
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    [loss] = 1.07, [lr/lr_group0] = 0.0417, [nll_loss] = 1.07: 100%|██████████| 20/20 [13:29<00:00, 40.50s/it]
    [2022-03-23 14:00:21.712] Training finished in 13 minutes 30 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 14:00:22.078] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:00:22.170] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.51s/it][2022-03-23 14:00:23.239] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:00:23.307] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:02<00:00,  1.46s/it]
    [2022-03-23 14:00:24.644] Inference finished in 2.93 seconds.
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     25%|██▌       | 3/12 [00:05<00:15,  1.69s/it][2022-03-23 14:00:29.908] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     33%|███▎      | 4/12 [00:06<00:12,  1.62s/it][2022-03-23 14:00:31.429] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     42%|████▏     | 5/12 [00:07<00:10,  1.44s/it][2022-03-23 14:00:32.555] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:09<00:08,  1.47s/it][2022-03-23 14:00:34.066] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:10<00:06,  1.35s/it][2022-03-23 14:00:35.190] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:12<00:05,  1.41s/it][2022-03-23 14:00:36.719] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:00:36.787] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:13<00:04,  1.44s/it][2022-03-23 14:00:38.227] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:00:38.296] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 14:00:39.439] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 14:00:40.946] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:17<00:00,  1.47s/it]
    [2022-03-23 14:00:42.326] Inference finished in 17.7 seconds.
    [2022-03-23 14:00:42.340] No q_layer_op_list found, will not save it.
    [2022-03-23 14:00:42.369] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-4924.pt".
    [2022-03-23 14:00:42.371]
    + [acc/test] = 66.667
    + [acc/valid] = 63.333
    + [acc/valid/max] = 76.667
    + [loss] = 1.0742
    + [loss/test] = 1.0121
    + [loss/valid] = 1.0645
    + [lr/lr_group0] = 0.041728
    + [nll_loss] = 1.0742
    [2022-03-23 14:00:42.374] Estimated time left: 2 hours 18 minutes 34 seconds.
    [2022-03-23 14:00:42.378] Epoch finished in 13 minutes 50 seconds.
    [2022-03-23 14:00:42.380] Epoch 6/15 started.
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    [loss] = 1.02, [lr/lr_group0] = 0.0375, [nll_loss] = 1.02: 100%|██████████| 20/20 [13:07<00:00, 39.37s/it]
    [2022-03-23 14:13:49.708] Training finished in 13 minutes 7 seconds.
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    [2022-03-23 14:13:50.184] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 14:13:51.314] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:02<00:00,  1.47s/it]
    [2022-03-23 14:13:52.652] Inference finished in 2.94 seconds.
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     42%|████▏     | 5/12 [00:08<00:10,  1.56s/it][2022-03-23 14:14:00.758] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:09<00:08,  1.41s/it][2022-03-23 14:14:01.886] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:10<00:07,  1.46s/it][2022-03-23 14:14:03.430] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:12<00:05,  1.47s/it][2022-03-23 14:14:04.928] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:13<00:04,  1.48s/it][2022-03-23 14:14:06.444] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:14:06.510] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     83%|████████▎ | 10/12 [00:15<00:02,  1.49s/it][2022-03-23 14:14:07.953] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:14:08.264] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:17<00:01,  1.57s/it][2022-03-23 14:14:09.709] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:14:09.771] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:18<00:00,  1.57s/it]
    [2022-03-23 14:14:11.469] Inference finished in 18.8 seconds.
    [2022-03-23 14:14:11.474] No q_layer_op_list found, will not save it.
    [2022-03-23 14:14:11.489] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-5880.pt".
    [2022-03-23 14:14:11.491]
    + [acc/test] = 66.333
    + [acc/valid] = 60
    + [acc/valid/max] = 76.667
    + [loss] = 1.0234
    + [loss/test] = 1.0198
    + [loss/valid] = 1.0777
    + [lr/lr_group0] = 0.0375
    + [nll_loss] = 1.0234
    [2022-03-23 14:14:11.493] Estimated time left: 2 hours 4 minutes 9 seconds.
    [2022-03-23 14:14:11.495] Epoch finished in 13 minutes 29 seconds.
    [2022-03-23 14:14:11.497] Epoch 7/15 started.
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    [loss] = 0.934, [lr/lr_group0] = 0.0327, [nll_loss] = 0.934: 100%|██████████| 20/20 [13:25<00:00, 40.27s/it]
    [2022-03-23 14:27:36.866] Training finished in 13 minutes 25 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 14:27:37.265] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:27:37.336] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.90s/it][2022-03-23 14:27:38.791] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:27:38.855] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.67s/it]
    [2022-03-23 14:27:40.212] Inference finished in 3.34 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 14:27:40.663] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:27:40.729] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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      8%|▊         | 1/12 [00:01<00:17,  1.56s/it][2022-03-23 14:27:41.794] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:27:41.862] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     17%|█▋        | 2/12 [00:02<00:13,  1.30s/it][2022-03-23 14:27:42.916] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:27:42.980] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     25%|██▌       | 3/12 [00:04<00:12,  1.40s/it][2022-03-23 14:27:44.437] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:27:44.506] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     33%|███▎      | 4/12 [00:05<00:10,  1.29s/it][2022-03-23 14:27:45.558] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:27:45.625] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     42%|████▏     | 5/12 [00:06<00:09,  1.37s/it][2022-03-23 14:27:47.063] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:27:47.131] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:08<00:08,  1.50s/it][2022-03-23 14:27:48.808] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:27:48.870] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:10<00:07,  1.50s/it][2022-03-23 14:27:50.319] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:27:50.384] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:11<00:06,  1.50s/it][2022-03-23 14:27:51.825] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:27:51.890] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:13<00:04,  1.51s/it][2022-03-23 14:27:53.344] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:27:53.410] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     83%|████████▎ | 10/12 [00:14<00:03,  1.51s/it][2022-03-23 14:27:54.855] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:27:54.924] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:16<00:01,  1.52s/it][2022-03-23 14:27:56.399] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:27:56.464] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:17<00:00,  1.49s/it]
    [2022-03-23 14:27:58.139] Inference finished in 17.9 seconds.
    [2022-03-23 14:27:58.156] No q_layer_op_list found, will not save it.
    [2022-03-23 14:27:58.171] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-6872.pt".
    [2022-03-23 14:27:58.173]
    + [acc/test] = 68.667
    + [acc/valid] = 60
    + [acc/valid/max] = 76.667
    + [loss] = 0.9337
    + [loss/test] = 0.997
    + [loss/valid] = 1.0683
    + [lr/lr_group0] = 0.032725
    + [nll_loss] = 0.9337
    [2022-03-23 14:27:58.176] Estimated time left: 1 hour 50 minutes 20 seconds.
    [2022-03-23 14:27:58.178] Epoch finished in 13 minutes 46 seconds.
    [2022-03-23 14:27:58.181] Epoch 8/15 started.
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    [loss] = 0.957, [lr/lr_group0] = 0.0276, [nll_loss] = 0.957: 100%|██████████| 20/20 [13:17<00:00, 39.89s/it]
    [2022-03-23 14:41:15.898] Training finished in 13 minutes 17 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 14:41:16.308] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:41:16.381] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.90s/it][2022-03-23 14:41:17.832] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:41:17.897] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.67s/it]
    [2022-03-23 14:41:19.264] Inference finished in 3.36 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 14:41:19.750] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:41:19.844] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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      8%|▊         | 1/12 [00:01<00:17,  1.61s/it][2022-03-23 14:41:20.909] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:41:20.986] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     17%|█▋        | 2/12 [00:03<00:15,  1.56s/it][2022-03-23 14:41:22.440] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:41:22.520] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     25%|██▌       | 3/12 [00:04<00:13,  1.55s/it][2022-03-23 14:41:23.965] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:41:24.039] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     33%|███▎      | 4/12 [00:06<00:12,  1.54s/it][2022-03-23 14:41:25.488] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:41:25.557] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     42%|████▏     | 5/12 [00:07<00:10,  1.53s/it][2022-03-23 14:41:26.996] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:41:27.064] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:09<00:09,  1.52s/it][2022-03-23 14:41:28.510] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:41:28.575] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:10<00:07,  1.52s/it][2022-03-23 14:41:30.023] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:41:30.092] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:12<00:06,  1.52s/it][2022-03-23 14:41:31.541] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:41:31.612] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:13<00:04,  1.47s/it][2022-03-23 14:41:32.895] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:41:32.976] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     83%|████████▎ | 10/12 [00:14<00:02,  1.37s/it][2022-03-23 14:41:34.034] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:41:34.101] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:16<00:01,  1.41s/it][2022-03-23 14:41:35.550] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:41:35.622] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:18<00:00,  1.50s/it]
    [2022-03-23 14:41:37.326] Inference finished in 18 seconds.
    [2022-03-23 14:41:37.330] No q_layer_op_list found, will not save it.
    [2022-03-23 14:41:37.344] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-7864.pt".
    [2022-03-23 14:41:37.346]
    + [acc/test] = 67.667
    + [acc/valid] = 60
    + [acc/valid/max] = 76.667
    + [loss] = 0.95734
    + [loss/test] = 0.99281
    + [loss/valid] = 1.0654
    + [lr/lr_group0] = 0.027613
    + [nll_loss] = 0.95734
    [2022-03-23 14:41:37.348] Estimated time left: 1 hour 36 minutes 25 seconds.
    [2022-03-23 14:41:37.350] Epoch finished in 13 minutes 39 seconds.
    [2022-03-23 14:41:37.352] Epoch 9/15 started.
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    [loss] = 0.961, [lr/lr_group0] = 0.0224, [nll_loss] = 0.961: 100%|██████████| 20/20 [12:58<00:00, 38.91s/it]
    [2022-03-23 14:54:35.493] Training finished in 12 minutes 58 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 14:54:35.877] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:54:35.954] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.50s/it][2022-03-23 14:54:37.020] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:54:37.096] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:02<00:00,  1.47s/it]
    [2022-03-23 14:54:38.446] Inference finished in 2.95 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 14:54:38.911] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:54:38.989] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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      8%|▊         | 1/12 [00:01<00:21,  1.98s/it][2022-03-23 14:54:40.455] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:54:40.531] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     17%|█▋        | 2/12 [00:03<00:16,  1.61s/it][2022-03-23 14:54:41.809] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:54:41.889] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     25%|██▌       | 3/12 [00:04<00:14,  1.58s/it][2022-03-23 14:54:43.344] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:54:43.419] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     33%|███▎      | 4/12 [00:06<00:12,  1.55s/it][2022-03-23 14:54:44.853] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:54:44.920] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     42%|████▏     | 5/12 [00:07<00:09,  1.40s/it][2022-03-23 14:54:45.973] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:54:46.058] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:08<00:07,  1.31s/it][2022-03-23 14:54:47.115] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:54:47.179] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:10<00:06,  1.38s/it][2022-03-23 14:54:48.637] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:54:48.702] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:11<00:05,  1.42s/it][2022-03-23 14:54:50.142] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:54:50.211] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:13<00:04,  1.45s/it][2022-03-23 14:54:51.663] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:54:51.727] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     83%|████████▎ | 10/12 [00:14<00:02,  1.47s/it][2022-03-23 14:54:53.169] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:54:53.239] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:15<00:01,  1.36s/it][2022-03-23 14:54:54.292] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 14:54:54.367] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:17<00:00,  1.47s/it]
    [2022-03-23 14:54:56.051] Inference finished in 17.6 seconds.
    [2022-03-23 14:54:56.057] No q_layer_op_list found, will not save it.
    [2022-03-23 14:54:56.072] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-8820.pt".
    [2022-03-23 14:54:56.074]
    + [acc/test] = 67.667
    + [acc/valid] = 60
    + [acc/valid/max] = 76.667
    + [loss] = 0.96125
    + [loss/test] = 0.994
    + [loss/valid] = 1.0689
    + [lr/lr_group0] = 0.022387
    + [nll_loss] = 0.96125
    [2022-03-23 14:54:56.076] Estimated time left: 1 hour 22 minutes 14 seconds.
    [2022-03-23 14:54:56.078] Epoch finished in 13 minutes 18 seconds.
    [2022-03-23 14:54:56.080] Epoch 10/15 started.
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    [loss] = 1.03, [lr/lr_group0] = 0.0173, [nll_loss] = 1.03: 100%|██████████| 20/20 [13:15<00:00, 39.76s/it]
    [2022-03-23 15:08:11.385] Training finished in 13 minutes 15 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 15:08:11.774] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:08:11.861] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.91s/it][2022-03-23 15:08:13.325] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:08:13.401] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.68s/it]
    [2022-03-23 15:08:14.756] Inference finished in 3.36 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 15:08:15.197] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:08:15.296] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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      8%|▊         | 1/12 [00:01<00:17,  1.60s/it][2022-03-23 15:08:16.377] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:08:16.452] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     17%|█▋        | 2/12 [00:03<00:16,  1.68s/it][2022-03-23 15:08:18.117] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:08:18.188] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     25%|██▌       | 3/12 [00:04<00:12,  1.43s/it][2022-03-23 15:08:19.243] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:08:19.320] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     33%|███▎      | 4/12 [00:05<00:10,  1.31s/it][2022-03-23 15:08:20.377] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:08:20.460] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     42%|████▏     | 5/12 [00:07<00:09,  1.39s/it][2022-03-23 15:08:21.897] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:08:21.972] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:08<00:08,  1.43s/it][2022-03-23 15:08:23.414] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:08:23.496] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:10<00:07,  1.46s/it][2022-03-23 15:08:24.937] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:08:25.012] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:11<00:05,  1.48s/it][2022-03-23 15:08:26.455] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:08:26.542] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:13<00:04,  1.49s/it][2022-03-23 15:08:27.985] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:08:28.067] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     83%|████████▎ | 10/12 [00:14<00:02,  1.39s/it][2022-03-23 15:08:29.126] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:08:29.202] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:15<00:01,  1.43s/it][2022-03-23 15:08:30.653] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:08:30.723] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:17<00:00,  1.47s/it]
    [2022-03-23 15:08:32.419] Inference finished in 17.7 seconds.
    [2022-03-23 15:08:32.435] No q_layer_op_list found, will not save it.
    [2022-03-23 15:08:32.450] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-9812.pt".
    [2022-03-23 15:08:32.453]
    + [acc/test] = 68.333
    + [acc/valid] = 56.667
    + [acc/valid/max] = 76.667
    + [loss] = 1.0284
    + [loss/test] = 0.98941
    + [loss/valid] = 1.0661
    + [lr/lr_group0] = 0.017275
    + [nll_loss] = 1.0284
    [2022-03-23 15:08:32.456] Estimated time left: 1 hour 8 minutes 27 seconds.
    [2022-03-23 15:08:32.459] Epoch finished in 13 minutes 36 seconds.
    [2022-03-23 15:08:32.462] Epoch 11/15 started.
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    [loss] = 0.942, [lr/lr_group0] = 0.0125, [nll_loss] = 0.942: 100%|██████████| 20/20 [13:27<00:00, 40.38s/it]
    [2022-03-23 15:22:00.038] Training finished in 13 minutes 27 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 15:22:00.434] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:22:00.517] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.91s/it][2022-03-23 15:22:01.975] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:22:02.057] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.68s/it]
    [2022-03-23 15:22:03.415] Inference finished in 3.37 seconds.
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    [2022-03-23 15:22:03.982] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 15:22:05.134] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 15:22:06.649] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:09<00:09,  1.54s/it][2022-03-23 15:22:12.715] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:22:12.790] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:10<00:07,  1.54s/it][2022-03-23 15:22:14.243] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:22:14.335] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:12<00:06,  1.61s/it][2022-03-23 15:22:16.025] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:22:16.113] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:14<00:04,  1.59s/it][2022-03-23 15:22:17.570] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:22:17.648] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     83%|████████▎ | 10/12 [00:15<00:02,  1.45s/it][2022-03-23 15:22:18.707] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:22:18.782] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:16<00:01,  1.47s/it][2022-03-23 15:22:20.229] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:22:20.307] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:18<00:00,  1.55s/it]
    [2022-03-23 15:22:22.018] Inference finished in 18.6 seconds.
    [2022-03-23 15:22:22.021] No q_layer_op_list found, will not save it.
    [2022-03-23 15:22:22.039] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-10804.pt".
    [2022-03-23 15:22:22.045]
    + [acc/test] = 70
    + [acc/valid] = 56.667
    + [acc/valid/max] = 76.667
    + [loss] = 0.94174
    + [loss/test] = 0.98321
    + [loss/valid] = 1.059
    + [lr/lr_group0] = 0.0125
    + [nll_loss] = 0.94174
    [2022-03-23 15:22:22.047] Estimated time left: 54 minutes 57 seconds.
    [2022-03-23 15:22:22.052] Epoch finished in 13 minutes 49 seconds.
    [2022-03-23 15:22:22.053] Epoch 12/15 started.
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    [loss] = 0.871, [lr/lr_group0] = 0.00827, [nll_loss] = 0.871: 100%|██████████| 20/20 [13:05<00:00, 39.29s/it]
    [2022-03-23 15:35:27.839] Training finished in 13 minutes 5 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 15:35:28.256] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:35:28.343] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.55s/it][2022-03-23 15:35:29.420] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:35:29.498] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.52s/it]
    [2022-03-23 15:35:30.884] Inference finished in 3.04 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 15:35:31.405] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:35:31.499] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 15:35:46.893] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 15:35:48.430] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:19<00:00,  1.60s/it]
    [2022-03-23 15:35:50.131] Inference finished in 19.2 seconds.
    [2022-03-23 15:35:50.144] No q_layer_op_list found, will not save it.
    [2022-03-23 15:35:50.160] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-11760.pt".
    [2022-03-23 15:35:50.162]
    + [acc/test] = 67.667
    + [acc/valid] = 56.667
    + [acc/valid/max] = 76.667
    + [loss] = 0.87124
    + [loss/test] = 0.98594
    + [loss/valid] = 1.0628
    + [lr/lr_group0] = 0.0082717
    + [nll_loss] = 0.87124
    [2022-03-23 15:35:50.165] Estimated time left: 40 minutes 51 seconds.
    [2022-03-23 15:35:50.167] Epoch finished in 13 minutes 28 seconds.
    [2022-03-23 15:35:50.169] Epoch 13/15 started.
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    [loss] = 0.971, [lr/lr_group0] = 0.00477, [nll_loss] = 0.971: 100%|██████████| 20/20 [13:15<00:00, 39.77s/it]
    [2022-03-23 15:49:05.507] Training finished in 13 minutes 15 seconds.
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    [2022-03-23 15:49:05.986] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 15:49:07.514] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.68s/it]
    [2022-03-23 15:49:08.876] Inference finished in 3.37 seconds.
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     75%|███████▌  | 9/12 [00:13<00:04,  1.44s/it][2022-03-23 15:49:22.555] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:49:22.634] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 15:49:24.152] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:16<00:01,  1.36s/it][2022-03-23 15:49:25.205] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 15:49:25.274] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:17<00:00,  1.47s/it]
    [2022-03-23 15:49:26.580] Inference finished in 17.7 seconds.
    [2022-03-23 15:49:26.595] No q_layer_op_list found, will not save it.
    [2022-03-23 15:49:26.620] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-12752.pt".
    [2022-03-23 15:49:26.621]
    + [acc/test] = 68.667
    + [acc/valid] = 56.667
    + [acc/valid/max] = 76.667
    + [loss] = 0.97094
    + [loss/test] = 0.98713
    + [loss/valid] = 1.0617
    + [lr/lr_group0] = 0.0047746
    + [nll_loss] = 0.97094
    [2022-03-23 15:49:26.624] Estimated time left: 27 minutes 11 seconds.
    [2022-03-23 15:49:26.625] Epoch finished in 13 minutes 36 seconds.
    [2022-03-23 15:49:26.626] Epoch 14/15 started.
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    [loss] = 1.04, [lr/lr_group0] = 0.00216, [nll_loss] = 1.04: 100%|██████████| 20/20 [13:21<00:00, 40.07s/it]
    [2022-03-23 16:02:47.994] Training finished in 13 minutes 21 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 16:02:48.400] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 16:02:48.485] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.92s/it][2022-03-23 16:02:49.947] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 16:02:50.026] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.69s/it]
    [2022-03-23 16:02:51.393] Inference finished in 3.39 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 16:02:51.899] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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    [2022-03-23 16:02:53.131] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 16:02:54.865] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     25%|██▌       | 3/12 [00:04<00:14,  1.62s/it][2022-03-23 16:02:56.328] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     33%|███▎      | 4/12 [00:06<00:12,  1.57s/it][2022-03-23 16:02:57.829] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:13<00:04,  1.43s/it][2022-03-23 16:03:05.001] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 16:03:05.076] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 16:03:06.598] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:16<00:01,  1.48s/it][2022-03-23 16:03:08.053] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 16:03:08.130] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:18<00:00,  1.54s/it]
    [2022-03-23 16:03:09.839] Inference finished in 18.4 seconds.
    [2022-03-23 16:03:09.847] No q_layer_op_list found, will not save it.
    [2022-03-23 16:03:09.861] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-13744.pt".
    [2022-03-23 16:03:09.863]
    + [acc/test] = 69.333
    + [acc/valid] = 56.667
    + [acc/valid/max] = 76.667
    + [loss] = 1.0415
    + [loss/test] = 0.98499
    + [loss/valid] = 1.0606
    + [lr/lr_group0] = 0.0021614
    + [nll_loss] = 1.0415
    [2022-03-23 16:03:09.865] Estimated time left: 13 minutes 37 seconds.
    [2022-03-23 16:03:09.867] Epoch finished in 13 minutes 43 seconds.
    [2022-03-23 16:03:09.869] Epoch 15/15 started.
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    [loss] = 0.894, [lr/lr_group0] = 0.000546, [nll_loss] = 0.894: 100%|██████████| 20/20 [12:51<00:00, 38.56s/it]
    [2022-03-23 16:16:01.019] Training finished in 12 minutes 51 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 16:16:01.424] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 16:16:01.504] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.92s/it][2022-03-23 16:16:02.959] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 16:16:03.036] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.69s/it]
    [2022-03-23 16:16:04.407] Inference finished in 3.38 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 16:16:04.890] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     25%|██▌       | 3/12 [00:04<00:13,  1.55s/it][2022-03-23 16:16:09.111] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     33%|███▎      | 4/12 [00:06<00:12,  1.54s/it][2022-03-23 16:16:10.631] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 16:16:10.709] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     42%|████▏     | 5/12 [00:07<00:10,  1.53s/it][2022-03-23 16:16:12.159] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 16:16:12.232] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:09<00:09,  1.53s/it][2022-03-23 16:16:13.683] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 16:16:13.759] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:10<00:07,  1.53s/it][2022-03-23 16:16:15.202] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 16:16:15.282] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:12<00:06,  1.53s/it][2022-03-23 16:16:16.731] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 16:16:16.804] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:13<00:04,  1.40s/it][2022-03-23 16:16:17.860] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 16:16:17.943] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     83%|████████▎ | 10/12 [00:14<00:02,  1.32s/it][2022-03-23 16:16:19.001] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 16:16:19.075] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:16<00:01,  1.46s/it][2022-03-23 16:16:20.768] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:18<00:00,  1.51s/it]
    [2022-03-23 16:16:22.536] Inference finished in 18.1 seconds.
    [2022-03-23 16:16:22.550] No q_layer_op_list found, will not save it.
    [2022-03-23 16:16:22.564] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-14700.pt".
    [2022-03-23 16:16:22.566]
    + [acc/test] = 69
    + [acc/valid] = 56.667
    + [acc/valid/max] = 76.667
    + [loss] = 0.89366
    + [loss/test] = 0.98533
    + [loss/valid] = 1.0605
    + [lr/lr_group0] = 0.00054631
    + [nll_loss] = 0.89366
    [2022-03-23 16:16:22.567] Epoch finished in 13 minutes 12 seconds.
    [2022-03-23 16:16:22.569] 15 epochs of training finished in 3 hours 24 minutes 57 seconds.

```python
configs.model.arch.pruning_method=None
num_forward2, accu2 = train_with_configs(configs)
```

    [2022-03-23 16:16:39.545] Only use the front 500 images as TRAIN set.
    [2022-03-23 16:16:39.608] Only use the front 30 images as VALID set.
    [2022-03-23 16:16:39.622] Only use the front 300 images as TEST set.
    [2022-03-23 16:16:39.634] Not use any pruning

    /usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
      cpuset_checked))
    ibmqfactory.load_account:WARNING:2022-03-23 16:16:40,153: Credentials are already in use. The existing account in the session will be replaced.

    [2022-03-23 16:16:42.263] Cannot use MIT backend, roll back to open
    [2022-03-23 16:16:42.269] Model Size: 36
    [2022-03-23 16:16:42.275] /usr/bin/python3 /usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py -f /root/.local/share/jupyter/runtime/kernel-16eed591-b4a6-4540-88ba-fa352954d776.json
    [2022-03-23 16:16:42.277] Training started: "runs/probabilistic_gradient_pruning/".
    model:
      arch:
        n_nodes: 1
        n_wires: 4
        node_archs: [{'n_wires': 4, 'encoder_op_list_name': '4x4_ryzxy', 'n_blocks': 3, 'n_layers_per_block': 3, 'q_layer_name': 'barren_0'}]
        down_sample_kernel_size: 6
        act_norm: None
        pruning_method: None
        accumulation_window_size: 1
        pruning_window_size: 2
        pruning_ratio: 0.5
      name: q_multifc0
      transpile_before_run: False
      load_op_list: False
    dataset:
      input_name: image
      target_name: digit
    optimizer:
      name: adam
      lr: 0.05
      weight_decay: 0.0001
      lambda_lr: 0.01
    run:
      n_epochs: 15
      bsz: 25
      workers_per_gpu: 8
      device: gpu
    debug:
      pdb: False
      set_seed: True
      seed: 42
    callbacks: [{'callback': 'InferenceRunner', 'split': 'valid', 'subcallbacks': [{'metrics': 'CategoricalAccuracy', 'name': 'acc/valid'}, {'metrics': 'NLLError', 'name': 'loss/valid'}]}, {'callback': 'InferenceRunner', 'split': 'test', 'subcallbacks': [{'metrics': 'CategoricalAccuracy', 'name': 'acc/test'}, {'metrics': 'NLLError', 'name': 'loss/test'}]}, {'callback': 'MaxSaver', 'name': 'acc/valid'}, {'callback': 'Saver', 'max_to_keep': 10}]
    qiskit:
      use_qiskit: True
      use_real_qc: False
      use_qiskit_train: True
      use_qiskit_valid: True
      backend_name: None
      noise_model_name: None
      basis_gates_name: None
      n_shots: 8192
      initial_layout: None
      seed_transpiler: 42
      seed_simulator: 42
      optimization_level: 0
      est_success_rate: False
      max_jobs: 1
    [2022-03-23 16:16:43.012] Epoch 1/15 started.
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    [loss] = 1.04, [lr/lr_group0] = 0.05, [nll_loss] = 1.04: 100%|██████████| 20/20 [19:46<00:00, 59.32s/it]
    [2022-03-23 16:36:29.500] Training finished in 19 minutes 46 seconds.
      0%|          | 0/2 [00:00<?, ?it/s]

    /usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 8 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.
      cpuset_checked))

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    [loss] = 1.04, [lr/lr_group0] = 0.0327, [nll_loss] = 1.04: 100%|██████████| 20/20 [19:10<00:00, 57.51s/it]
    [2022-03-23 18:34:29.230] Training finished in 19 minutes 10 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 18:34:29.645] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:34:29.719] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.91s/it][2022-03-23 18:34:31.169] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:34:31.234] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.67s/it]
    [2022-03-23 18:34:32.583] Inference finished in 3.35 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 18:34:33.072] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:34:33.155] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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      8%|▊         | 1/12 [00:01<00:17,  1.61s/it][2022-03-23 18:34:34.225] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:34:34.297] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     17%|█▋        | 2/12 [00:03<00:15,  1.56s/it][2022-03-23 18:34:35.742] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:34:35.818] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     25%|██▌       | 3/12 [00:04<00:13,  1.54s/it][2022-03-23 18:34:37.259] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:34:37.329] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     33%|███▎      | 4/12 [00:06<00:12,  1.53s/it][2022-03-23 18:34:38.773] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:34:38.850] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     42%|████▏     | 5/12 [00:07<00:10,  1.52s/it][2022-03-23 18:34:40.290] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:34:40.362] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:09<00:09,  1.53s/it][2022-03-23 18:34:41.824] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:34:41.905] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:10<00:07,  1.40s/it][2022-03-23 18:34:42.961] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:34:43.025] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:11<00:05,  1.31s/it][2022-03-23 18:34:44.080] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:34:44.144] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:12<00:03,  1.25s/it][2022-03-23 18:34:45.201] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:34:45.268] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     83%|████████▎ | 10/12 [00:14<00:02,  1.40s/it][2022-03-23 18:34:46.935] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:34:47.011] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:15<00:01,  1.43s/it][2022-03-23 18:34:48.450] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:34:48.513] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:17<00:00,  1.43s/it]
    [2022-03-23 18:34:49.818] Inference finished in 17.2 seconds.
    [2022-03-23 18:34:49.833] No q_layer_op_list found, will not save it.
    [2022-03-23 18:34:49.853] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-10220.pt".
    [2022-03-23 18:34:49.858]
    + [acc/test] = 67.333
    + [acc/valid] = 60
    + [acc/valid/max] = 60
    + [loss] = 1.0416
    + [loss/test] = 0.98833
    + [loss/valid] = 1.0702
    + [lr/lr_group0] = 0.032725
    + [nll_loss] = 1.0416
    [2022-03-23 18:34:49.862] Estimated time left: 2 hours 37 minutes 50 seconds.
    [2022-03-23 18:34:49.867] Epoch finished in 19 minutes 30 seconds.
    [2022-03-23 18:34:49.869] Epoch 8/15 started.
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    [loss] = 0.97, [lr/lr_group0] = 0.0276, [nll_loss] = 0.97: 100%|██████████| 20/20 [19:36<00:00, 58.82s/it]
    [2022-03-23 18:54:26.370] Training finished in 19 minutes 36 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 18:54:26.809] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:54:26.883] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:02<00:02,  2.22s/it][2022-03-23 18:54:28.614] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:54:28.696] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.85s/it]
    [2022-03-23 18:54:30.079] Inference finished in 3.71 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 18:54:30.587] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:54:30.676] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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      8%|▊         | 1/12 [00:01<00:18,  1.66s/it][2022-03-23 18:54:31.757] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:54:31.835] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     17%|█▋        | 2/12 [00:03<00:15,  1.58s/it][2022-03-23 18:54:33.288] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:54:33.355] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     25%|██▌       | 3/12 [00:04<00:13,  1.55s/it][2022-03-23 18:54:34.809] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:54:34.879] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     33%|███▎      | 4/12 [00:06<00:12,  1.55s/it][2022-03-23 18:54:36.344] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:54:36.413] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     42%|████▏     | 5/12 [00:07<00:10,  1.54s/it][2022-03-23 18:54:37.879] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:54:37.946] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:09<00:09,  1.54s/it][2022-03-23 18:54:39.415] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:54:39.483] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:10<00:07,  1.53s/it][2022-03-23 18:54:40.936] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:54:41.010] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:12<00:06,  1.53s/it][2022-03-23 18:54:42.459] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:54:42.526] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:13<00:04,  1.53s/it][2022-03-23 18:54:43.986] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:54:44.059] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
    Job Status: job has successfully run
     83%|████████▎ | 10/12 [00:15<00:03,  1.53s/it][2022-03-23 18:54:45.508] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 18:54:45.577] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:16<00:01,  1.53s/it][2022-03-23 18:54:47.037] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:19<00:00,  1.59s/it]
    [2022-03-23 18:54:49.109] Inference finished in 19 seconds.
    [2022-03-23 18:54:49.119] No q_layer_op_list found, will not save it.
    [2022-03-23 18:54:49.135] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/max-acc-valid.pt" (63.333).
    [2022-03-23 18:54:49.138] No q_layer_op_list found, will not save it.
    [2022-03-23 18:54:49.150] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-11680.pt".
    [2022-03-23 18:54:49.153]
    + [acc/test] = 67.333
    + [acc/valid] = 63.333
    + [acc/valid/max] = 63.333
    + [loss] = 0.96974
    + [loss/test] = 0.98923
    + [loss/valid] = 1.0744
    + [lr/lr_group0] = 0.027613
    + [nll_loss] = 0.96974
    [2022-03-23 18:54:49.155] Estimated time left: 2 hours 18 minutes 20 seconds.
    [2022-03-23 18:54:49.157] Epoch finished in 19 minutes 59 seconds.
    [2022-03-23 18:54:49.159] Epoch 9/15 started.
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    [loss] = 0.982, [lr/lr_group0] = 0.0224, [nll_loss] = 0.982: 100%|██████████| 20/20 [19:21<00:00, 58.06s/it]
    [2022-03-23 19:14:10.434] Training finished in 19 minutes 21 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 19:14:10.869] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:14:10.951] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.95s/it][2022-03-23 19:14:12.408] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:14:12.483] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.71s/it]
    [2022-03-23 19:14:13.856] Inference finished in 3.42 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 19:14:14.360] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:14:14.452] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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      8%|▊         | 1/12 [00:02<00:22,  2.04s/it][2022-03-23 19:14:15.925] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:14:15.993] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     17%|█▋        | 2/12 [00:03<00:17,  1.74s/it][2022-03-23 19:14:17.447] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:14:17.524] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     25%|██▌       | 3/12 [00:05<00:15,  1.76s/it][2022-03-23 19:14:19.227] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:14:19.296] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     33%|███▎      | 4/12 [00:06<00:13,  1.66s/it][2022-03-23 19:14:20.751] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:14:20.822] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     42%|████▏     | 5/12 [00:08<00:11,  1.62s/it][2022-03-23 19:14:22.291] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:14:22.359] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:09<00:09,  1.59s/it][2022-03-23 19:14:23.811] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:14:23.878] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:11<00:07,  1.57s/it][2022-03-23 19:14:25.340] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:12<00:06,  1.55s/it][2022-03-23 19:14:26.849] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:14:26.917] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:14<00:04,  1.54s/it][2022-03-23 19:14:28.382] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:14:28.455] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     83%|████████▎ | 10/12 [00:16<00:03,  1.54s/it][2022-03-23 19:14:29.905] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:14:29.976] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:17<00:01,  1.54s/it][2022-03-23 19:14:31.439] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:14:31.514] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:19<00:00,  1.61s/it]
    [2022-03-23 19:14:33.242] Inference finished in 19.4 seconds.
    [2022-03-23 19:14:33.251] No q_layer_op_list found, will not save it.
    [2022-03-23 19:14:33.274] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-13140.pt".
    [2022-03-23 19:14:33.280]
    + [acc/test] = 68.333
    + [acc/valid] = 63.333
    + [acc/valid/max] = 63.333
    + [loss] = 0.98214
    + [loss/test] = 0.983
    + [loss/valid] = 1.0687
    + [lr/lr_group0] = 0.022387
    + [nll_loss] = 0.98214
    [2022-03-23 19:14:33.282] Estimated time left: 1 hour 58 minutes 18 seconds.
    [2022-03-23 19:14:33.287] Epoch finished in 19 minutes 44 seconds.
    [2022-03-23 19:14:33.289] Epoch 10/15 started.
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    [loss] = 0.944, [lr/lr_group0] = 0.0173, [nll_loss] = 0.944: 100%|██████████| 20/20 [19:50<00:00, 59.51s/it]
    [2022-03-23 19:34:23.474] Training finished in 19 minutes 50 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 19:34:23.911] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:34:24.002] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.98s/it][2022-03-23 19:34:25.481] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:34:25.559] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.71s/it]
    [2022-03-23 19:34:26.914] Inference finished in 3.43 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 19:34:27.421] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:34:27.515] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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      8%|▊         | 1/12 [00:02<00:22,  2.03s/it][2022-03-23 19:34:28.976] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:34:29.054] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     17%|█▋        | 2/12 [00:03<00:17,  1.74s/it][2022-03-23 19:34:30.516] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:34:30.587] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 19:34:32.111] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 19:34:33.242] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     42%|████▏     | 5/12 [00:07<00:10,  1.47s/it][2022-03-23 19:34:34.691] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:11<00:05,  1.29s/it][2022-03-23 19:34:38.470] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:34:38.545] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:13<00:04,  1.37s/it][2022-03-23 19:34:39.998] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:34:40.075] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     83%|████████▎ | 10/12 [00:14<00:02,  1.41s/it][2022-03-23 19:34:41.519] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:34:41.593] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:15<00:01,  1.40s/it][2022-03-23 19:34:42.899] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:34:42.972] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:17<00:00,  1.48s/it]
    [2022-03-23 19:34:44.669] Inference finished in 17.7 seconds.
    [2022-03-23 19:34:44.680] No q_layer_op_list found, will not save it.
    [2022-03-23 19:34:44.702] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-14600.pt".
    [2022-03-23 19:34:44.707]
    + [acc/test] = 67.333
    + [acc/valid] = 63.333
    + [acc/valid/max] = 63.333
    + [loss] = 0.94449
    + [loss/test] = 0.98667
    + [loss/valid] = 1.0679
    + [lr/lr_group0] = 0.017275
    + [nll_loss] = 0.94449
    [2022-03-23 19:34:44.708] Estimated time left: 1 hour 38 minutes 51 seconds.
    [2022-03-23 19:34:44.709] Epoch finished in 20 minutes 11 seconds.
    [2022-03-23 19:34:44.711] Epoch 11/15 started.
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    [loss] = 1.07, [lr/lr_group0] = 0.0125, [nll_loss] = 1.07: 100%|██████████| 20/20 [19:30<00:00, 58.51s/it]
    [2022-03-23 19:54:14.921] Training finished in 19 minutes 30 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 19:54:15.360] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:54:15.435] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.97s/it][2022-03-23 19:54:16.916] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:54:16.984] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 19:54:18.354] Inference finished in 3.43 seconds.
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    [2022-03-23 19:54:18.958] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 19:54:20.483] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 19:54:22.006] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 19:54:30.194] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:13<00:04,  1.46s/it][2022-03-23 19:54:31.627] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:54:31.694] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     83%|████████▎ | 10/12 [00:14<00:02,  1.36s/it][2022-03-23 19:54:32.750] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:54:32.825] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:15<00:01,  1.29s/it][2022-03-23 19:54:33.896] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 19:54:33.973] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:17<00:00,  1.44s/it]
    [2022-03-23 19:54:35.658] Inference finished in 17.3 seconds.
    [2022-03-23 19:54:35.674] No q_layer_op_list found, will not save it.
    [2022-03-23 19:54:35.692] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-16060.pt".
    [2022-03-23 19:54:35.697]
    + [acc/test] = 67
    + [acc/valid] = 63.333
    + [acc/valid/max] = 63.333
    + [loss] = 1.0701
    + [loss/test] = 0.98661
    + [loss/valid] = 1.0738
    + [lr/lr_group0] = 0.0125
    + [nll_loss] = 1.0701
    [2022-03-23 19:54:35.702] Estimated time left: 1 hour 19 minutes 14 seconds.
    [2022-03-23 19:54:35.703] Epoch finished in 19 minutes 50 seconds.
    [2022-03-23 19:54:35.708] Epoch 12/15 started.
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    [loss] = 0.98, [lr/lr_group0] = 0.00827, [nll_loss] = 0.98: 100%|██████████| 20/20 [19:29<00:00, 58.48s/it]
    [2022-03-23 20:14:05.419] Training finished in 19 minutes 29 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 20:14:05.853] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:14:05.938] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.56s/it][2022-03-23 20:14:07.005] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:14:07.078] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.50s/it]
    [2022-03-23 20:14:08.434] Inference finished in 3.01 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 20:14:08.935] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:14:09.023] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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      8%|▊         | 1/12 [00:02<00:22,  2.02s/it][2022-03-23 20:14:10.485] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:14:10.555] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    [2022-03-23 20:14:12.110] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     25%|██▌       | 3/12 [00:04<00:13,  1.47s/it][2022-03-23 20:14:13.167] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:14:13.242] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     33%|███▎      | 4/12 [00:05<00:10,  1.33s/it][2022-03-23 20:14:14.297] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:14:14.369] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     42%|████▏     | 5/12 [00:06<00:08,  1.26s/it][2022-03-23 20:14:15.429] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:14:15.509] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:08<00:08,  1.36s/it][2022-03-23 20:14:16.967] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:14:17.048] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:10<00:07,  1.41s/it][2022-03-23 20:14:18.487] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:14:18.557] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:11<00:05,  1.32s/it][2022-03-23 20:14:19.613] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:14:19.684] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:12<00:03,  1.26s/it][2022-03-23 20:14:20.746] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:14:20.826] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     83%|████████▎ | 10/12 [00:14<00:02,  1.42s/it][2022-03-23 20:14:22.525] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:14:22.599] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:15<00:01,  1.45s/it][2022-03-23 20:14:24.045] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:14:24.113] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:17<00:00,  1.45s/it]
    [2022-03-23 20:14:25.808] Inference finished in 17.4 seconds.
    [2022-03-23 20:14:25.821] No q_layer_op_list found, will not save it.
    [2022-03-23 20:14:25.837] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-17520.pt".
    [2022-03-23 20:14:25.838]
    + [acc/test] = 68.333
    + [acc/valid] = 60
    + [acc/valid/max] = 63.333
    + [loss] = 0.97979
    + [loss/test] = 0.98503
    + [loss/valid] = 1.0691
    + [lr/lr_group0] = 0.0082717
    + [nll_loss] = 0.97979
    [2022-03-23 20:14:25.847] Estimated time left: 59 minutes 30 seconds.
    [2022-03-23 20:14:25.851] Epoch finished in 19 minutes 50 seconds.
    [2022-03-23 20:14:25.852] Epoch 13/15 started.
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    [loss] = 0.984, [lr/lr_group0] = 0.00477, [nll_loss] = 0.984: 100%|██████████| 20/20 [19:10<00:00, 57.52s/it]
    [2022-03-23 20:33:36.341] Training finished in 19 minutes 10 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 20:33:36.794] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:33:36.870] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.57s/it][2022-03-23 20:33:37.941] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:33:38.007] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.52s/it]
    [2022-03-23 20:33:39.397] Inference finished in 3.05 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 20:33:39.902] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:33:39.995] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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      8%|▊         | 1/12 [00:01<00:18,  1.64s/it][2022-03-23 20:33:41.066] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:33:41.135] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     17%|█▋        | 2/12 [00:02<00:13,  1.34s/it][2022-03-23 20:33:42.191] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:33:42.270] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     25%|██▌       | 3/12 [00:03<00:11,  1.25s/it][2022-03-23 20:33:43.328] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:33:43.397] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     33%|███▎      | 4/12 [00:05<00:09,  1.20s/it][2022-03-23 20:33:44.457] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:33:44.537] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     42%|████▏     | 5/12 [00:06<00:08,  1.18s/it][2022-03-23 20:33:45.594] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:33:45.663] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:07<00:06,  1.16s/it][2022-03-23 20:33:46.720] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:33:46.798] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:08<00:06,  1.28s/it][2022-03-23 20:33:48.261] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:33:48.329] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:09<00:04,  1.23s/it][2022-03-23 20:33:49.382] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:33:49.464] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:11<00:03,  1.33s/it][2022-03-23 20:33:50.912] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:33:50.980] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     83%|████████▎ | 10/12 [00:13<00:02,  1.46s/it][2022-03-23 20:33:52.660] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:33:52.747] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:14<00:01,  1.48s/it][2022-03-23 20:33:54.204] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:33:54.279] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:16<00:00,  1.35s/it]
    [2022-03-23 20:33:55.586] Inference finished in 16.2 seconds.
    [2022-03-23 20:33:55.595] No q_layer_op_list found, will not save it.
    [2022-03-23 20:33:55.611] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-18980.pt".
    [2022-03-23 20:33:55.613]
    + [acc/test] = 67.667
    + [acc/valid] = 63.333
    + [acc/valid/max] = 63.333
    + [loss] = 0.98411
    + [loss/test] = 0.98492
    + [loss/valid] = 1.0692
    + [lr/lr_group0] = 0.0047746
    + [nll_loss] = 0.98411
    [2022-03-23 20:33:55.616] Estimated time left: 39 minutes 38 seconds.
    [2022-03-23 20:33:55.618] Epoch finished in 19 minutes 29 seconds.
    [2022-03-23 20:33:55.620] Epoch 14/15 started.
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    [loss] = 1, [lr/lr_group0] = 0.00216, [nll_loss] = 1: 100%|██████████| 20/20 [19:05<00:00, 57.28s/it]
    [2022-03-23 20:53:01.141] Training finished in 19 minutes 5 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 20:53:01.574] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:53:01.660] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:01<00:01,  1.96s/it][2022-03-23 20:53:03.131] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:53:03.200] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.72s/it]
    [2022-03-23 20:53:04.584] Inference finished in 3.44 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 20:53:05.109] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:53:05.201] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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      8%|▊         | 1/12 [00:01<00:18,  1.67s/it][2022-03-23 20:53:06.283] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:53:06.354] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     17%|█▋        | 2/12 [00:03<00:15,  1.59s/it][2022-03-23 20:53:07.817] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:53:07.887] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     25%|██▌       | 3/12 [00:04<00:14,  1.56s/it][2022-03-23 20:53:09.342] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:53:09.420] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     33%|███▎      | 4/12 [00:06<00:12,  1.55s/it][2022-03-23 20:53:10.876] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:53:10.946] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     42%|████▏     | 5/12 [00:07<00:10,  1.54s/it][2022-03-23 20:53:12.388] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:53:12.470] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:09<00:09,  1.54s/it][2022-03-23 20:53:13.921] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:53:13.993] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:10<00:07,  1.53s/it][2022-03-23 20:53:15.451] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:53:15.520] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:12<00:06,  1.53s/it][2022-03-23 20:53:16.978] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:53:17.059] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:13<00:04,  1.41s/it][2022-03-23 20:53:18.116] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:53:18.191] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     83%|████████▎ | 10/12 [00:15<00:03,  1.52s/it][2022-03-23 20:53:19.876] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:53:19.952] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:16<00:01,  1.40s/it][2022-03-23 20:53:21.019] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 20:53:21.091] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:17<00:00,  1.48s/it]
    [2022-03-23 20:53:22.405] Inference finished in 17.8 seconds.
    [2022-03-23 20:53:22.410] No q_layer_op_list found, will not save it.
    [2022-03-23 20:53:22.427] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-20440.pt".
    [2022-03-23 20:53:22.429]
    + [acc/test] = 67.667
    + [acc/valid] = 63.333
    + [acc/valid/max] = 63.333
    + [loss] = 1.0041
    + [loss/test] = 0.98459
    + [loss/valid] = 1.0707
    + [lr/lr_group0] = 0.0021614
    + [nll_loss] = 1.0041
    [2022-03-23 20:53:22.431] Estimated time left: 19 minutes 45 seconds.
    [2022-03-23 20:53:22.433] Epoch finished in 19 minutes 26 seconds.
    [2022-03-23 20:53:22.435] Epoch 15/15 started.
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    [loss] = 0.915, [lr/lr_group0] = 0.000546, [nll_loss] = 0.915: 100%|██████████| 20/20 [19:30<00:00, 58.53s/it]
    [2022-03-23 21:12:53.004] Training finished in 19 minutes 30 seconds.
      0%|          | 0/2 [00:00<?, ?it/s][2022-03-23 21:12:53.440] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 21:12:53.770] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 1/2 [00:02<00:02,  2.20s/it][2022-03-23 21:12:55.225] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 21:12:55.293] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 2/2 [00:03<00:00,  1.82s/it]
    [2022-03-23 21:12:56.670] Inference finished in 3.66 seconds.
      0%|          | 0/12 [00:00<?, ?it/s][2022-03-23 21:12:57.178] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 21:12:57.267] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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      8%|▊         | 1/12 [00:01<00:18,  1.64s/it][2022-03-23 21:12:58.334] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 21:12:58.404] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     17%|█▋        | 2/12 [00:02<00:13,  1.34s/it][2022-03-23 21:12:59.470] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 21:12:59.538] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     25%|██▌       | 3/12 [00:04<00:12,  1.42s/it][2022-03-23 21:13:00.986] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 21:13:01.054] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     33%|███▎      | 4/12 [00:05<00:11,  1.45s/it][2022-03-23 21:13:02.491] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 21:13:02.560] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     42%|████▏     | 5/12 [00:07<00:10,  1.48s/it][2022-03-23 21:13:04.006] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 21:13:04.086] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     50%|█████     | 6/12 [00:08<00:08,  1.49s/it][2022-03-23 21:13:05.526] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 21:13:05.594] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     58%|█████▊    | 7/12 [00:10<00:07,  1.50s/it][2022-03-23 21:13:07.040] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 21:13:07.109] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     67%|██████▋   | 8/12 [00:11<00:06,  1.50s/it][2022-03-23 21:13:08.546] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 21:13:08.613] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     75%|███████▌  | 9/12 [00:12<00:04,  1.38s/it][2022-03-23 21:13:09.668] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 21:13:09.734] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     83%|████████▎ | 10/12 [00:14<00:02,  1.43s/it][2022-03-23 21:13:11.196] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 21:13:11.263] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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     92%|█████████▏| 11/12 [00:16<00:01,  1.45s/it][2022-03-23 21:13:12.702] Before transpile: {'depth': 22, 'size': 69, 'width': 8, 'n_single_gates': 56, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 24, 'rz': 16, 'rx': 16, 'cz': 9, 'measure': 4}}
    [2022-03-23 21:13:12.780] After transpile: {'depth': 15, 'size': 41, 'width': 8, 'n_single_gates': 28, 'n_two_gates': 9, 'n_three_more_gates': 0, 'n_gates_dict': {'ry': 8, 'rz': 4, 'rx': 4, 'u3': 12, 'cz': 9, 'measure': 4}}
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    100%|██████████| 12/12 [00:18<00:00,  1.50s/it]
    [2022-03-23 21:13:14.722] Inference finished in 18.1 seconds.
    [2022-03-23 21:13:14.729] No q_layer_op_list found, will not save it.
    [2022-03-23 21:13:14.747] Checkpoint saved: "runs/probabilistic_gradient_pruning/checkpoints/step-21900.pt".
    [2022-03-23 21:13:14.752]
    + [acc/test] = 68
    + [acc/valid] = 63.333
    + [acc/valid/max] = 63.333
    + [loss] = 0.91464
    + [loss/test] = 0.98475
    + [loss/valid] = 1.0677
    + [lr/lr_group0] = 0.00054631
    + [nll_loss] = 0.91464
    [2022-03-23 21:13:14.756] Epoch finished in 19 minutes 52 seconds.
    [2022-03-23 21:13:14.757] 15 epochs of training finished in 4 hours 56 minutes 32 seconds.

### Plot and compare the accuracy curves

```python
import matplotlib.pyplot as plt

plt.plot(num_forward1, accu1, marker='o', label="with gradient pruning")
plt.plot(num_forward2, accu2, marker='o', label="without gradient pruning")
plt.ylabel("test accuracy")
plt.xlabel("num of forwards")
plt.legend()
plt.show()
```