# How to Implement a Quantum Self-Attention Transformer on Dynex

In this guide, we’ll walk through the implementation of a Quantum Self-Attention Transformer using quantum computing principles on the Dynex neuromorphic quantum computing cloud. This approach replicates the self-attention mechanism found in classical transformers, but with the added power of quantum computation, aiming to enhance tasks like Natural Language Processing (NLP).

## 1. Introduction to the Quantum Self-Attention Transformer

The Quantum Self-Attention Transformer applies quantum circuits to mimic the self-attention mechanism, a critical component of classical transformers. Self-attention allows models to weigh the importance of different words in a sentence, which is crucial for tasks like translation and text generation. By leveraging quantum operations on the Dynex neuromorphic quantum cloud, this quantum transformer can potentially process this information more efficiently by exploiting quantum parallelism and interference.

**Importance of Quantum Transformers:** Quantum transformers hold significant potential in NLP due to their ability to handle complex computations in parallel. Classical transformers are powerful but require immense computational resources as model sizes grow. Quantum transformers, particularly those run on Dynex’s quantum computing cloud, could dramatically reduce the computational load, enabling faster and more efficient processing. This is especially important for large language models (LLMs) where the need to balance speed, accuracy, and resource usage is critical. Additionally, the quantum version of transformers can offer novel ways to approach problems, opening up possibilities that are infeasible with classical computing alone.

## 2. Preparing the Input Data

Before the quantum circuit can process the data, we need to convert the input sentences into a form that the quantum computer can understand.

**Tokenization**: Each sentence is broken down into individual words, and all characters are converted to lowercase to maintain consistency in processing.**Word Embeddings**: Each word is then mapped to an 8-dimensional vector using a Word2Vec model. Word2Vec is a method that transforms words into numerical vectors in a continuous vector space, where words with similar meanings have closer vector representations

`from nltk.tokenize import word_tokenize`

from gensim.models import Word2Vec

sentences = ["Dynex powers Quantum entanglement", "Neuromorphic networks process qubits"]

tokenization = [word_tokenize(sentence.lower()) for sentence in sentences]

word2vec = Word2Vec(sentences=tokenization, vector_size=8, window=5, min_count=1, workers=4)

## 3. Quantum Circuit Initialization

The quantum circuit is initialized with 8 qubits. Each qubit corresponds to one of the 8 dimensions of the word embeddings. These qubits will be used to represent and process the input vectors.

**Basis Embedding**: The binary form of each word embedding is embedded into the quantum state using`qml.BasisEmbedding`

. This step encodes classical data (the word embeddings) into quantum states, preparing them for quantum operations.

`import pennylane as qml`

from pennylane import numpy as np

qubits = 8

dev = qml.device("default.qubit", wires=qubits)

@qml.qnode(dev)

def QuantumSelfAttention(inputs):

biIn = [1 if x >= 0 else 0 for x in inputs]

qml.BasisEmbedding(biIn, wires=range(qubits))

## 4. Quantum Self-Attention Layers

This is the core of the quantum self-attention mechanism. The circuit performs multiple layers of operations to capture the complex relationships between the elements of the input vector.

**Rotation Gates**:`RX`

,`RY`

, and`RZ`

gates are applied to each qubit. These gates rotate the qubits around the X, Y, and Z axes respectively. The angles of rotation are determined by the values in the input vector. This step effectively processes each feature of the word embeddings, manipulating the quantum state to capture relationships between different dimensions.**Entanglement**:`CRZ`

and`CNOT`

gates create entanglement between qubits, allowing the circuit to consider interactions between different features. Entanglement is a uniquely quantum phenomenon that links the states of qubits, so the state of one qubit depends on the state of another.

`for layer in range(3):`

for i in range(qubits):

qml.RX(inputs[i % len(inputs)] * (layer + 1), wires=i)

qml.RY(inputs[(i + 1) % len(inputs)] * (layer + 1), wires=i)

qml.RZ(inputs[(i + 2) % len(inputs)] * (layer + 1), wires=i)

for i in range(qubits - 1):

qml.CRZ(np.pi / (layer + 2), wires=[i, (i + 1) % qubits])

qml.CNOT(wires=[i, (i + 1) % qubits])

## 5. Fourier Transform and Grover’s Operator

The Quantum Fourier Transform (QFT) and its inverse are applied to move the quantum states into and out of the frequency domain. This is a key operation in quantum algorithms, allowing for efficient processing of periodic functions.

**Grover’s Operator**: This operator is used for amplifying the probability of the correct quantum states. It’s crucial in quantum search algorithms, and here it helps in focusing on the most relevant aspects of the input.

`qml.QFT(wires=range(qubits))`

qml.adjoint(qml.QFT)(wires=range(qubits))

qml.GroverOperator(wires=range(qubits))

## 6. Final Quantum Operations

These operations further manipulate the quantum state to prepare it for measurement.

**Hadamard and T Gates**: The Hadamard gate creates superposition, while the T gate applies a phase shift. These gates, combined with additional`RZ`

rotations, adjust the quantum state to emphasize the features that will be measured.**Basis Embedding**: Reapplying the Basis Embedding ensures that the quantum state is ready for the final measurement.

`for i in range(qubits):`

qml.Hadamard(wires=i)

qml.T(wires=i)

qml.RZ(inputs[i % len(inputs)], wires=i)

qml.BasisEmbedding(biIn, wires=range(qubits))

return [qml.expval(qml.PauliZ(wires=i)) for i in range(qubits)]

## 7. Generating New Sentences

The outputs from the quantum circuit are processed using a softmax function, which converts them into attention weights. These weights are then used to generate a new sentence by combining the quantum-processed outputs with the original word embeddings.

**Softmax Function**: Softmax normalizes the output so that the attention weights sum to 1, making them suitable for probabilistic interpretation.**Sentence Generation**: The new sentence is generated by selecting words that are most similar to the quantum-enhanced word embeddings.

`def softmax(x):`

return np.exp(x) / np.sum(np.exp(x), axis=0)

def GenerateSentence(input_):

tokens = word_tokenize(input_.lower())

iEmbeddings = np.array([word2vec.wv[word] for word in tokens])

attOutputs = []

for embedding in iEmbeddings:

attOUT = QuantumSelfAttention(embedding)

softOUT = softmax(attOUT)

attOutputs.append(softOUT)

# Sentence generation logic goes here

return ' '.join(generated[:4])

## 8. Running the Model

Finally, you can run the model on your input sentences to generate quantum-enhanced text. The entire computation is performed on the Dynex neuromorphic quantum computing cloud, utilizing its recently introduced support for quantum circuits. This platform enables the execution of complex quantum algorithms, making the Quantum Self-Attention Transformer practical and scalable for NLP tasks.

`input_ = np.random.choice(sentences)`

generated = GenerateSentence(input_)

print(f"Generated: {generated}")

# Conclusion

The Quantum Self-Attention Transformer circuit combines the principles of quantum computing with the self-attention mechanism to process and generate sentences. By following this detailed guide, you can implement your own quantum self-attention models, experimenting with how quantum operations can enhance NLP tasks. This approach could lead to more efficient processing and potentially open new avenues in the development of quantum-enhanced language models.

**Why Quantum Transformers Matter:** Quantum transformers represent a significant step forward in both quantum computing and machine learning. Classical transformers have revolutionized NLP, but they require enormous computational resources. As these models scale, the demand for computational power becomes a bottleneck. Quantum transformers, particularly those run on Dynex’s quantum computing cloud, could alleviate these limitations by providing exponential speedups for certain operations, allowing for more efficient training and inference. Furthermore, quantum transformers could uncover new patterns and relationships in data that classical models might miss, thanks to the unique properties of quantum states. This could lead to breakthroughs not only in NLP but in a wide range of fields where understanding and processing complex data are crucial.

# About Dynex

Dynex is the world’s only accessible neuromorphic quantum computing cloud for solving real-world problems, at scale. The company began as an informal project in September 2020 in collaboration amongst a community of extraordinary minds and quickly evolved into a technological leader ready to scale into global markets. The Dynex n.quantum computing cloud performs quantum computing based algorithms without limitation, executing calculations with unparalleled speed and efficiency, surpassing usual quantum computing constraints. Dynex is dedicated to pushing the boundaries of technology to create sustainable, secure, and innovative solutions that address complex challenges and drive progress. For more information, visit dynex.co.