GPT on a Quantum Computer (2403.09418v1)

Abstract: LLMs such as ChatGPT have transformed how we interact with and understand the capabilities of AI. However, the intersection of LLMs with the burgeoning field of Quantum Machine Learning (QML) is only in its nascent stages. This paper presents an exploration of this niche by detailing a comprehensive framework for implementing the foundational Transformer architecture -- integral to ChatGPT -- within a quantum computing paradigm. We meticulously design quantum circuits that implement adapted versions of the transformer's core components and the generative pre-training phase. By integrating quantum computing with LLMs, we aspire to open new avenues for research in QML and contribute to the ongoing evolution of AI technologies.

• The paper implements a quantum version of the GPT architecture, adapting core components like multi-head self-attention and feed-forward networks using quantum circuits.
• It details the methodology for converting classical operations, including input encoding, masked self-attention, and residual connections, into quantum-friendly procedures.
• The research highlights future implications for quantum machine learning, suggesting enhanced efficiency and novel strategies for parameter optimization on quantum hardware.

Implementing Generative Pre-trained Transformer (GPT) Architecture on Quantum Computers

Introduction

The integration of Quantum Computing (QC) with LLMs represents a frontier in computational research. The Generative Pre-trained Transformer (GPT) architecture, a significant advancement in NLP, has shown promise in various tasks such as text generation, translation, and summarization. This paper explores the implementation of GPT's foundational architecture on quantum computers, focusing on key components like the multi-head masked self-attention mechanism, feed-forward networks, residual connections, and the generative pre-training phase.

Quantum Implementation of GPT Components

Input Encoding

The transition from classical to quantum implementation begins with the encoding of input data into quantum states. Input vectors are mapped to quantum states via amplitude encoding, facilitating their processing on quantum circuits. This encoding leverages two quantum registers to represent the indices and features of the input data.

Attention Mechanism

The core of GPT's architecture, the multi-head self-attention mechanism, is adapted for quantum computing. Quantum circuits are designed to perform the computation of attention scores between pairs of input vectors, utilizing quantum versions of linear transformations for query, key, and value vectors. The adaptation omits some elements like the softmax function, focusing instead on capturing the essential functionalities of attention through quantum operations.

Masked Self-Attention

To implement the masking operation in a quantum-friendly manner, the attention scores are adjusted to zero for future positions in the sequence, mirroring the masking effect. This adaptation ensures the generation of contextually relevant predictions while adhering to the principles of causal masking in LLMs.

Feed-Forward Networks

The quantum implementation of feed-forward networks within GPT involves a sequence of quantum operations that mimic the classical network's behavior. This includes the evaluation of activation functions like ReLU at a quantum level, showcasing a novel approach to implementing non-linear transformations in quantum circuits.

Residual Connections

Quantum circuits facilitate the addition of input vectors and attention vectors, achieving the functionality of residual connections. This mechanism promotes the flow of information across different layers of the model, enhancing its learning capacity without directly translating classical arithmetic operations onto quantum hardware.

Generative Pre-training on Quantum Computers

The generative pre-training phase, crucial for the model's ability to generate coherent text, is adapted for quantum computation by utilizing quantum circuits to evolve the model's parameters based on the training data. This process entails the quantum evaluation of loss functions and the adaptation of parameter adjustment strategies suitable for quantum hardware.

Future Work and Implications

The quantum implementation of GPT opens up new avenues for research in quantum machine learning (QML) and artificial intelligence. Future developments may involve optimizing quantum circuits for efficiency, exploring quantum algorithms for parameter optimization, and expanding the model's capabilities through quantum-enhanced functionalities.

This exploration signifies a step towards realizing more powerful and efficient AI models by leveraging quantum computing's potential. It highlights the interdisciplinary nature of advancing AI technologies and sets the groundwork for future breakthroughs in integrating quantum computing with state-of-the-art machine learning frameworks.