LLM-Powered Quantum Code Transpilation

TL;DR

This paper proposes using Large Language Models to automate and improve the translation of quantum programs between different SDKs, enhancing interoperability without manual rule creation.

Contribution

It introduces a novel LLM-based approach for quantum code transpilation that is flexible, scalable, and reduces manual effort compared to traditional rule-based methods.

Findings

LLMs can effectively translate quantum code between SDKs.

The approach preserves functional equivalence across translations.

It offers a scalable solution for quantum software portability.

Abstract

There exist various Software Development Kits (SDKs) tailored to different quantum computing platforms. These are known as Quantum SDKs (QSDKs). Examples include but are not limited to Qiskit, Cirq, and PennyLane. However, this diversity presents significant challenges for interoperability and cross-platform development of hybrid quantum-classical software systems. Traditional rule-based transpilers for translating code between QSDKs are time-consuming to design and maintain, requiring deep expertise and rigid mappings in the source and destination code. In this study, we explore the use of Large Language Models (LLMs) as a flexible and automated solution. Leveraging their pretrained knowledge and contextual reasoning capabilities, we position LLMs as programming language-agnostic transpilers capable of converting quantum programs from one QSDK to another while preserving functional equivalence. Our approach eliminates the need for manually defined transformation rules and offers a scalable solution to quantum software portability. This work represents a step toward enabling intelligent, general-purpose transpilation in the quantum computing ecosystem.