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GPT on a Quantum Computer

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arxiv 2403.09418 v1 pith:AVGRMZ5O submitted 2024-03-14 quant-ph

classification quant-ph
keywords quantumllmschatgptcomputingtransformeradaptedarchitectureartificial
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Efficient Generation of Parameterised Quantum Circuits from Large Texts

    quant-ph 2025-05 conditional novelty 7.0 of 10

    A tree-based pregroup representation lets a quantum NLP pipeline convert long documents, up to 6410 words, into trainable parameterised quantum circuits much more reliably than the previous CCG-based pipeline.

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