Synthesis of Quantum Simulators by Compilation

Quantum simulation plays a critical role in advancing our understanding and development of quantum algorithms, quantum computing hardware, and quantum information science. Despite the availability of various quantum circuit simulators, they often face challenges in terms of maintainability and extensibility. In this paper, we introduce Lightweight Functional Quantum Simulator (LFQS), a compilation-based framework that addresses these issues by leveraging an intermediate language to synthesize efficient quantum simulators in C and CUDA. The intermediate language exploits the underlying structured sparsity behind the matrix representation of quantum gates. Our framework generates efficient code for multi-core CPUs and GPUs and outperforms state-of-the-art simulators, such as Qiskit, Catalyst, and QuEST, as well as a representative of dense tensor frameworks, NumPy, in both micro-benchmark and macro-benchmark circuits.

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Published
2025-02-22
DOI
https://doi.org/10.1145/3696443.3708949
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
0.00

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article

Synthesis of Quantum Simulators by Compilation

Mina Doosti, Mahshid Delavar, Amir Shaikhha, Meisam Tarabkhah
Quantum Computing Algorithms and Architecture
article

Synthesis of Quantum Simulators by Compilation

Mina Doosti, Mahshid Delavar, Amir Shaikhha, Meisam Tarabkhah
article en

Abstract

Quantum simulation plays a critical role in advancing our understanding and development of quantum algorithms, quantum computing hardware, and quantum information science. Despite the availability of various quantum circuit simulators, they often face challenges in terms of maintainability and extensibility. In this paper, we introduce Lightweight Functional Quantum Simulator (LFQS), a compilation-based framework that addresses these issues by leveraging an intermediate language to synthesize efficient quantum simulators in C and CUDA. The intermediate language exploits the underlying structured sparsity behind the matrix representation of quantum gates. Our framework generates efficient code for multi-core CPUs and GPUs and outperforms state-of-the-art simulators, such as Qiskit, Catalyst, and QuEST, as well as a representative of dense tensor frameworks, NumPy, in both micro-benchmark and macro-benchmark circuits.

University of Sheffield (GB), University of Edinburgh (GB)
Engineering and Physical Sciences Research Council
Openalex Percentile: Top 99%
Quantum Computing Algorithms and Architecture
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Synthesis of Quantum Simulators by Compilation — Mina Doosti, Mahshid Delavar, et al. · (2025) | TGRS Research Map | TGRS