```python
!pip install pylatexenc qiskit[machine-learning]
```

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```python
import numpy as np
import matplotlib.pyplot as plt

from qiskit import Aer, QuantumCircuit
from qiskit.utils import QuantumInstance
from qiskit.circuit import ParameterVector
from qiskit.circuit.parametertable import ParameterView
from qiskit.circuit.library import PauliFeatureMap, EfficientSU2
from qiskit.algorithms.optimizers import COBYLA

from qiskit_machine_learning.neural_networks import CircuitQNN
from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier

import gensim
import torch
import torch.nn as nn
import pickle
from sklearn.decomposition import PCA

import itertools

from IPython.display import clear_output
```

```python
from google.colab import drive
drive.mount('/content/drive')
```

    Mounted at /content/drive

# Preprocess Word Embeddings

```python
with open(r"/content/drive/MyDrive/pre_ft_vectors", "rb") as input_file:
  embeddings_dict = pickle.load(input_file)
```

# Preprocess Data

```python
with open("/content/drive/MyDrive/amazon_reviews_data.txt") as f:
  raw_corpus = f.readlines()
```

```python
corpus = [ { "sentence": [''.join(e for e in word if e.isalnum()).lower() for word in sentence.split()[:-1]], "sentiment": int(sentence.split()[-1]) }  for sentence in raw_data ]
corpus
```

    [{'sentence': ['so',
       'there',
       'is',
       'no',
       'way',
       'for',
       'me',
       'to',
       'plug',
       'it',
       'in',
       'here',
       'in',
       'the',
       'us',
       'unless',
       'i',
       'go',
       'by',
       'a',
       'converter'],
      'sentiment': 0},
     {'sentence': ['good', 'case', 'excellent', 'value'], 'sentiment': 1},
     {'sentence': ['great', 'for', 'the', 'jawbone'], 'sentiment': 1},
     {'sentence': ['tied',
       'to',
       'charger',
       'for',
       'conversations',
       'lasting',
       'more',
       'than',
       '45',
       'minutesmajor',
       'problems'],
      'sentiment': 0},
     {'sentence': ['the', 'mic', 'is', 'great'], 'sentiment': 1},
     {'sentence': ['i',
       'have',
       'to',
       'jiggle',
       'the',
       'plug',
       'to',
       'get',
       'it',
       'to',
       'line',
       'up',
       'right',
       'to',
       'get',
       'decent',
       'volume'],
      'sentiment': 0},
     {'sentence': ['if',
       'you',
       'have',
       'several',
       'dozen',
       'or',
       'several',
       'hundred',
       'contacts',
       'then',
       'imagine',
       'the',
       'fun',
       'of',
       'sending',
       'each',
       'of',
       'them',
       'one',
       'by',
       'one'],
      'sentiment': 0},
     {'sentence': ['if', 'you', 'are', 'razr', 'owneryou', 'must', 'have', 'this'],
      'sentiment': 1},
     {'sentence': ['needless', 'to', 'say', 'i', 'wasted', 'my', 'money'],
      'sentiment': 0},
     {'sentence': ['what', 'a', 'waste', 'of', 'money', 'and', 'time'],
      'sentiment': 0},
     {'sentence': ['and', 'the', 'sound', 'quality', 'is', 'great'],
      'sentiment': 1},
     {'sentence': ['he',
       'was',
       'very',
       'impressed',
       'when',
       'going',
       'from',
       'the',
       'original',
       'battery',
       'to',
       'the',
       'extended',
       'battery'],
      'sentiment': 1},
     {'sentence': ['if',
       'the',
       'two',
       'were',
       'seperated',
       'by',
       'a',
       'mere',
       '5',
       'ft',
       'i',
       'started',
       'to',
       'notice',
       'excessive',
       'static',
       'and',
       'garbled',
       'sound',
       'from',
       'the',
       'headset'],
      'sentiment': 0},
     {'sentence': ['very', 'good', 'quality', 'though'], 'sentiment': 1},
     {'sentence': ['the',
       'design',
       'is',
       'very',
       'odd',
       'as',
       'the',
       'ear',
       'clip',
       'is',
       'not',
       'very',
       'comfortable',
       'at',
       'all'],
      'sentiment': 0},
     {'sentence': ['highly',
       'recommend',
       'for',
       'any',
       'one',
       'who',
       'has',
       'a',
       'blue',
       'tooth',
       'phone'],
      'sentiment': 1},
     {'sentence': ['i', 'advise', 'everyone', 'do', 'not', 'be', 'fooled'],
      'sentiment': 0},
     {'sentence': ['so', 'far', 'so', 'good'], 'sentiment': 1},
     {'sentence': ['works', 'great'], 'sentiment': 1},
     {'sentence': ['it',
       'clicks',
       'into',
       'place',
       'in',
       'a',
       'way',
       'that',
       'makes',
       'you',
       'wonder',
       'how',
       'long',
       'that',
       'mechanism',
       'would',
       'last'],
      'sentiment': 0},
     {'sentence': ['i',
       'went',
       'on',
       'motorolas',
       'website',
       'and',
       'followed',
       'all',
       'directions',
       'but',
       'could',
       'not',
       'get',
       'it',
       'to',
       'pair',
       'again'],
      'sentiment': 0},
     {'sentence': ['i',
       'bought',
       'this',
       'to',
       'use',
       'with',
       'my',
       'kindle',
       'fire',
       'and',
       'absolutely',
       'loved',
       'it'],
      'sentiment': 1},
     {'sentence': ['the', 'commercials', 'are', 'the', 'most', 'misleading'],
      'sentiment': 0},
     {'sentence': ['i',
       'have',
       'yet',
       'to',
       'run',
       'this',
       'new',
       'battery',
       'below',
       'two',
       'bars',
       'and',
       'thats',
       'three',
       'days',
       'without',
       'charging'],
      'sentiment': 1},
     {'sentence': ['i',
       'bought',
       'it',
       'for',
       'my',
       'mother',
       'and',
       'she',
       'had',
       'a',
       'problem',
       'with',
       'the',
       'battery'],
      'sentiment': 0},
     {'sentence': ['great', 'pocket', 'pc', '', 'phone', 'combination'],
      'sentiment': 1},
     {'sentence': ['ive',
       'owned',
       'this',
       'phone',
       'for',
       '7',
       'months',
       'now',
       'and',
       'can',
       'say',
       'that',
       'its',
       'the',
       'best',
       'mobile',
       'phone',
       'ive',
       'had'],
      'sentiment': 1},
     {'sentence': ['i',
       'didnt',
       'think',
       'that',
       'the',
       'instructions',
       'provided',
       'were',
       'helpful',
       'to',
       'me'],
      'sentiment': 0},
     {'sentence': ['people',
       'couldnt',
       'hear',
       'me',
       'talk',
       'and',
       'i',
       'had',
       'to',
       'pull',
       'out',
       'the',
       'earphone',
       'and',
       'talk',
       'on',
       'the',
       'phone'],
      'sentiment': 0},
     {'sentence': ['doesnt', 'hold', 'charge'], 'sentiment': 0},
     {'sentence': ['this',
       'is',
       'a',
       'simple',
       'little',
       'phone',
       'to',
       'use',
       'but',
       'the',
       'breakage',
       'is',
       'unacceptible'],
      'sentiment': 0},
     {'sentence': ['this',
       'product',
       'is',
       'ideal',
       'for',
       'people',
       'like',
       'me',
       'whose',
       'ears',
       'are',
       'very',
       'sensitive'],
      'sentiment': 1},
     {'sentence': ['it',
       'is',
       'unusable',
       'in',
       'a',
       'moving',
       'car',
       'at',
       'freeway',
       'speed'],
      'sentiment': 0},
     {'sentence': ['i',
       'have',
       'two',
       'more',
       'years',
       'left',
       'in',
       'this',
       'contract',
       'and',
       'i',
       'hate',
       'this',
       'phone'],
      'sentiment': 0},
     {'sentence': ['car',
       'charger',
       'as',
       'well',
       'as',
       'ac',
       'charger',
       'are',
       'included',
       'to',
       'make',
       'sure',
       'you',
       'never',
       'run',
       'out',
       'of',
       'juicehighy',
       'recommended'],
      'sentiment': 1},
     {'sentence': ['you',
       'need',
       'at',
       'least',
       '3',
       'mins',
       'to',
       'get',
       'to',
       'your',
       'phone',
       'book',
       'from',
       'the',
       'time',
       'you',
       'first',
       'turn',
       'on',
       'the',
       'phonebattery',
       'life',
       'is',
       'short'],
      'sentiment': 0},
     {'sentence': ['it', 'has', 'kept', 'up', 'very', 'well'], 'sentiment': 1},
     {'sentence': ['poor', 'talk', 'time', 'performance'], 'sentiment': 0},
     {'sentence': ['the',
       'case',
       'is',
       'great',
       'and',
       'works',
       'fine',
       'with',
       'the',
       '680'],
      'sentiment': 1},
     {'sentence': ['worthless', 'product'], 'sentiment': 0},
     {'sentence': ['it',
       'has',
       'a',
       'great',
       'camera',
       'thats',
       '2mp',
       'and',
       'the',
       'pics',
       'are',
       'nice',
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       'on',
       'the',
       'market',
       'yet'],
      'sentiment': 1},
     {'sentence': ['sweetest', 'phone'], 'sentiment': 1},
     {'sentence': ['oh', 'the', 'charger', 'seems', 'to', 'work', 'fine'],
      'sentiment': 1},
     {'sentence': ['it',
       'fits',
       'so',
       'securely',
       'that',
       'the',
       'ear',
       'hook',
       'does',
       'not',
       'even',
       'need',
       'to',
       'be',
       'used',
       'and',
       'the',
       'sound',
       'is',
       'better',
       'directed',
       'through',
       'your',
       'ear',
       'canal'],
      'sentiment': 1},
     {'sentence': ['not', 'enough', 'volume'], 'sentiment': 0},
     {'sentence': ['echo', 'problemvery', 'unsatisfactory'], 'sentiment': 0},
     {'sentence': ['you',
       'could',
       'only',
       'take',
       '2',
       'videos',
       'at',
       'a',
       'time',
       'and',
       'the',
       'quality',
       'was',
       'very',
       'poor'],
      'sentiment': 0},
     {'sentence': ['dont', 'waste', 'your', 'money'], 'sentiment': 0},
     {'sentence': ['i',
       'am',
       'going',
       'to',
       'have',
       'to',
       'be',
       'the',
       'first',
       'to',
       'negatively',
       'review',
       'this',
       'product'],
      'sentiment': 0},
     {'sentence': ['adapter',
       'does',
       'not',
       'provide',
       'enough',
       'charging',
       'current'],
      'sentiment': 0},
     {'sentence': ['there',
       'was',
       'so',
       'much',
       'hype',
       'over',
       'this',
       'phone',
       'that',
       'i',
       'assumed',
       'it',
       'was',
       'the',
       'best',
       'my',
       'mistake'],
      'sentiment': 0},
     {'sentence': ['you',
       'also',
       'cannot',
       'take',
       'pictures',
       'with',
       'it',
       'in',
       'the',
       'case',
       'because',
       'the',
       'lense',
       'is',
       'covered'],
      'sentiment': 0},
     {'sentence': ['phone', 'falls', 'out', 'easily'], 'sentiment': 0},
     {'sentence': ['it',
       'didnt',
       'work',
       'people',
       'can',
       'not',
       'hear',
       'me',
       'when',
       'i',
       'talk'],
      'sentiment': 0},
     {'sentence': ['the',
       'text',
       'messaging',
       'feature',
       'is',
       'really',
       'tricky',
       'to',
       'use'],
      'sentiment': 0},
     {'sentence': ['im',
       'really',
       'disappointed',
       'all',
       'i',
       'have',
       'now',
       'is',
       'a',
       'charger',
       'that',
       'doesnt',
       'work'],
      'sentiment': 0},
     {'sentence': ['painful', 'on', 'the', 'ear'], 'sentiment': 0},
     {'sentence': ['lasted', 'one', 'day', 'and', 'then', 'blew', 'up'],
      'sentiment': 0},
     {'sentence': ['disappointed'], 'sentiment': 0},
     {'sentence': ['kind', 'of', 'flops', 'around'], 'sentiment': 0},
     {'sentence': ['the',
       'screen',
       'does',
       'get',
       'smudged',
       'easily',
       'because',
       'it',
       'touches',
       'your',
       'ear',
       'and',
       'face'],
      'sentiment': 0},
     {'sentence': ['what',
       'a',
       'piece',
       'of',
       'junk',
       'i',
       'lose',
       'more',
       'calls',
       'on',
       'this',
       'phone'],
      'sentiment': 0},
     {'sentence': ['item', 'does', 'not', 'match', 'picture'], 'sentiment': 0},
     {'sentence': ['the',
       'only',
       'thing',
       'that',
       'disappoint',
       'me',
       'is',
       'the',
       'infra',
       'red',
       'port',
       'irda'],
      'sentiment': 0},
     {'sentence': ['you',
       'can',
       'not',
       'answer',
       'calls',
       'with',
       'the',
       'unit',
       'never',
       'worked',
       'once'],
      'sentiment': 0}]

# qTransformer Model

```python
class QuantumTransformer:
    def embed_sentence(self, sentence):
        embedded_sentence = np.array(
            [self.embeddings[word] for word in sentence]
        ).flatten()
        return np.pad(
            embedded_sentence, (0, self.sequence_length - embedded_sentence.size)
        )

    def __init__(
        self,
        sequence_length,
        embedding_dimension,
        embedding_dict,
        quantum_instance=Aer.get_backend("qasm_simulator"),
        reps=1,
        iterations=200,
        include_attention=True,
        include_residual=True,
    ):

        # Model Layers
        def attention_pair(i, j, l=0):
            qc = QuantumCircuit(2)
            qc.u(*ParameterVector("atn_%d_pair_%d%d_0" % (l, i, j), length=3), 0)
            qc.u(*ParameterVector("atn_%d_pair_%d%d_1" % (l, i, j), length=3), 1)
            qc.cx(1, 0)
            qc.u(*ParameterVector("atn_%d_pair_%d%d_2" % (l, i, j), length=3), 0)
            qc.u(*ParameterVector("atn_%d_pair_%d%d_3" % (l, i, j), length=3), 1)
            qc.cx(0, 1)
            qc.u(*ParameterVector("atn_%d_pair_%d%d_4" % (l, i, j), length=3), 0)
            qc.u(*ParameterVector("atn_%d_pair_%d%d_5" % (l, i, j), length=3), 1)
            qc.cx(1, 0)
            qc.u(*ParameterVector("atn_%d_pair_%d%d_6" % (l, i, j), length=3), 0)
            qc.u(*ParameterVector("atn_%d_pair_%d%d_7" % (l, i, j), length=3), 1)
            return qc.to_gate(label="Attention(%d, %d)" % (i, j))

        def attention(n, l=0):
            qc = QuantumCircuit(n)
            for pair in itertools.combinations(range(n), 2):
                qc.append(attention_pair(*pair, l=l), pair)
            return qc.to_gate(label="Attention")

        def residual(n):
            qc = QuantumCircuit(2 * n)
            for i in range(n):
                qc.cx(n + i, i)
            return qc.to_gate(label="Residual")

        # Classifier Utilities
        def parity(x):
            return "{:b}".format(x).count("1") % 2

        def callback_graph(weights, obj_func_eval):
            clear_output(wait=True)
            objective_func_vals.append(obj_func_eval)
            plt.title("Objective Function vs. Iteration")
            plt.xlabel("Iteration")
            plt.ylabel("Objective Function")
            plt.plot(range(len(objective_func_vals)), objective_func_vals)
            plt.show()

        self.sequence_length = sequence_length
        self.embedding_dimension = embedding_dimension

        n = self.sequence_length * self.embedding_dimension

        pca = PCA(n_components=self.embedding_dimension)
        self.embeddings = {}
        word_to_num = {}
        embeds_matrix = []

        for i, word in enumerate(embedding_dict.vocab.keys()):
            word_to_num[word] = i
            embeds_matrix.append(embedding_dict[word])

        embeds_matrix = np.array(embeds_matrix)
        embeds_matrix = pca.fit_transform(embeds_matrix)
        for word in embedding_dict.vocab.keys():
            self.embeddings[word] = embeds_matrix[word_to_num[word]]

        feature_map = PauliFeatureMap(n)
        if include_attention:
            attention_layers = [attention(n, l=i) for i in range(reps)]
        if include_residual:
            residual_layers = [residual(n) for _ in range(reps)]
        feed_forward_layers = [
            EfficientSU2(n, parameter_prefix=str(i)) for i in range(reps)
        ]

        self.circuit = QuantumCircuit(2 * n if include_residual else n)

        self.circuit.append(feature_map, range(n))
        if include_residual:
            self.circuit.append(feature_map, range(n, 2 * n))

        for i in range(reps):
            if include_attention:
                self.circuit.append(attention_layers[i], range(n))
            if include_residual:
                self.circuit.append(residual_layers[i], range(2 * n))
            self.circuit.append(feed_forward_layers[i], range(n))

        self.network = CircuitQNN(
            circuit=self.circuit,
            input_params=feature_map.parameters,
            weight_params=ParameterView(
                (
                    [parameter for l in attention_layers for parameter in l.params]
                    if include_attention
                    else []
                )
                + [parameter for l in feed_forward_layers for parameter in l.parameters]
            ),
            interpret=parity,
            output_shape=2,
            quantum_instance=quantum_instance,
        )

        self.classifier = NeuralNetworkClassifier(
            neural_network=self.network,
            optimizer=COBYLA(maxiter=iterations),
            callback=callback_graph,
        )

    def preprocess_data(self, corpus):
        filtered_sequences = []
        for sentence in corpus:
            sequences = [
                {
                    "sentence": sentence["sentence"][i : i + self.sequence_length],
                    "sentiment": sentence["sentiment"],
                }
                for i in range(0, len(sentence["sentence"]), self.sequence_length)
            ]
            for sequence in sequences:
                if all(word in self.embeddings for word in sequence["sentence"]):
                    filtered_sequences.append(sequence)
        filtered_sequences = [
            sentence for sentence in filtered_sequences if len(sentence["sentence"]) > 2
        ]

        vocab = []
        for sequence in filtered_sequences:
            vocab.extend(sequence["sentence"])
        vocab = list(set(vocab))

        X = np.array(
            [
                self.embed_sentence(sentence["sentence"])
                for sentence in filtered_sequences
            ]
        )
        Y = np.array(
            [
                sentence["sentiment"]
                for sentence in filtered_sequences
                if len(sentence["sentence"]) > 2
            ]
        )

        return X, Y

    def train(self, X, Y):
        plt.rcParams["figure.figsize"] = (12, 6)
        self.classifier.fit(X, Y)
        plt.rcParams["figure.figsize"] = (6, 4)

    def score(self, X, Y):
        return self.classifier.score(X, Y)

    def predict(self, sentence):
        print()
        return self.classifier.predict(
            self.embed_sentence(
                [
                    "".join(e for e in word if e.isalnum()).lower()
                    for word in sentence.split()
                ]
            )
        )

```

# Performance Evaluation

```python
EMBEDDING_DIMS = 1
TRAIN_DATA_SIZE = 50
SEQUENCE_LENGTH = 4
```

## w/o Attention

```python
transformer = QuantumTransformer(sequence_length=SEQUENCE_LENGTH, embedding_dimension=EMBEDDING_DIMS, embedding_dict=embeddings_dict, include_attention=0, include_residual=0)
transformer.circuit.draw("mpl")
```

```python
X, Y = transformer.preprocess_data(corpus)
```

```python
objective_func_vals = []
plt.rcParams["figure.figsize"] = (12, 6)
transformer.classifier.fit(X[:TRAIN_DATA_SIZE], Y[:TRAIN_DATA_SIZE])
plt.rcParams["figure.figsize"] = (6, 4)
```

```python
transformer.classifier.score(X[:TRAIN_DATA_SIZE], Y[:TRAIN_DATA_SIZE])
```

    0.78

```python
transformer.classifier.score(X[TRAIN_DATA_SIZE:2*TRAIN_DATA_SIZE], Y[TRAIN_DATA_SIZE:2*TRAIN_DATA_SIZE])
```

    0.52

## w/ Attention w/o Residual

```python
transformer = QuantumTransformer(sequence_length=SEQUENCE_LENGTH, embedding_dimension=EMBEDDING_DIMS, embedding_dict=embeddings_dict, reps=1, include_attention=1, include_residual=0)
transformer.circuit.draw("mpl")
```

```python
X, Y = transformer.preprocess_data(corpus)
```

```python
objective_func_vals = []
plt.rcParams["figure.figsize"] = (12, 6)
transformer.classifier.fit(X[:TRAIN_DATA_SIZE], Y[:TRAIN_DATA_SIZE])
plt.rcParams["figure.figsize"] = (6, 4)
```

```python
transformer.classifier.score(X[:TRAIN_DATA_SIZE], Y[:TRAIN_DATA_SIZE])
```

    0.8

```python
transformer.classifier.score(X[TRAIN_DATA_SIZE:2*TRAIN_DATA_SIZE], Y[TRAIN_DATA_SIZE:2*TRAIN_DATA_SIZE])
```

    0.46

## w/Attention w/Residual

```python
transformer = QuantumTransformer(sequence_length=SEQUENCE_LENGTH, embedding_dimension=EMBEDDING_DIMS, embedding_dict=embeddings_dict, reps=1, include_attention=1, include_residual=1)
transformer.circuit.draw("mpl")
```

```python
X, Y = transformer.preprocess_data(corpus)
```

```python
objective_func_vals = []
plt.rcParams["figure.figsize"] = (12, 6)
transformer.classifier.fit(X[:TRAIN_DATA_SIZE], Y[:TRAIN_DATA_SIZE])
plt.rcParams["figure.figsize"] = (6, 4)
```

```python
transformer.classifier.score(X[:TRAIN_DATA_SIZE], Y[:TRAIN_DATA_SIZE])
```

```python
transformer.classifier.score(X[TRAIN_DATA_SIZE:2*TRAIN_DATA_SIZE], Y[TRAIN_DATA_SIZE:2*TRAIN_DATA_SIZE])
```

# 2x qTransformer Blocks w/ Attention

```python
transformer = QuantumTransformer(sequence_length=SEQUENCE_LENGTH, embedding_dimension=EMBEDDING_DIMS, embedding_dict=embeddings_dict, reps=2, include_attention=1, include_residual=1)
transformer.circuit.draw("mpl")
```

```python
X, Y = transformer.preprocess_data(corpus)
```

```python
objective_func_vals = []
plt.rcParams["figure.figsize"] = (12, 6)
transformer.classifier.fit(X[:TRAIN_DATA_SIZE], Y[:TRAIN_DATA_SIZE])
plt.rcParams["figure.figsize"] = (6, 4)
```

```python
transformer.classifier.score(X[:TRAIN_DATA_SIZE], Y[:TRAIN_DATA_SIZE])
```

```python
transformer.classifier.score(X[TRAIN_DATA_SIZE:2*TRAIN_DATA_SIZE], Y[TRAIN_DATA_SIZE:2*TRAIN_DATA_SIZE])
```