GPT on a Quantum Computer

TL;DR

This paper explores implementing the core Transformer architecture of large language models like ChatGPT on quantum computers, aiming to pioneer quantum-enhanced AI capabilities.

Contribution

It introduces a comprehensive framework for adapting Transformer components and pre-training phases to quantum circuits, bridging LLMs and quantum computing.

Findings

Designed quantum circuits for Transformer components

Proposed quantum algorithms for pre-training LLMs

Opened new research directions in QML and AI

Abstract

Large Language Models (LLMs) such as ChatGPT have transformed how we interact with and understand the capabilities of Artificial Intelligence (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.