Programming tools for Analogue Quantum Computing in the High-Performance Computing Context -- A Review

TL;DR

This review paper surveys analogue quantum programming tools, assesses their readiness for high-performance computing integration, and identifies key gaps and future research directions in the field.

Contribution

It provides a comprehensive survey of analogue quantum software tools, introduces a readiness assessment system, and offers recommendations for advancing analogue quantum programming in HPC.

Findings

Identified critical gaps in analogue quantum programming tools.

Developed a classification and rating system for tool readiness.

Provided actionable recommendations for future research.

Abstract

Recent advances in quantum computing have brought us closer to realizing the potential of this transformative technology. While significant strides have been made in quantum error correction, many challenges persist, particularly in the realm of noise and scalability. Analogue quantum computing schemes, such as Analogue Hamiltonian Simulation and Quantum Annealing, offer a promising approach to address these limitations. By operating at a higher level of abstraction, these schemes can simplify the development of large-scale quantum algorithms. To fully harness the power of quantum computers, they must be seamlessly integrated with traditional high-performance computing (HPC) systems. While substantial research has focused on the integration of circuit-based quantum computers with HPC, the integration of analogue quantum computers remains relatively unexplored. This paper aims to bridge this gap by contributing in the following way: Comprehensive Survey: We conduct a comprehensive survey of existing quantum software tools with analogue capabilities. Readiness Assessment: We introduce a classification and rating system to assess the readiness of these tools for HPC integration. Gap Identification and Recommendations: We identify critical gaps in the landscape of analogue quantum programming models and propose actionable recommendations for future research and development.