Optimizing Quantum Circuits, Fast and Slow

Optimizing quantum circuits is critical: the number of quantum operations needs to be minimized for a successful evaluation of a circuit on a quantum processor. In this paper we unify two disparate ideas for optimizing quantum circuits, rewrite rules, which are fast standard optimizer passes, and unitary synthesis, which is slow, requiring a search through the space of circuits. We present a clean, unifying framework for thinking of rewriting and resynthesis as abstract circuit transformations. We then present a radically simple algorithm, guoq, for optimizing quantum circuits that exploits the synergies of rewriting and resynthesis. Our extensive evaluation demonstrates the ability of guoq to strongly outperform existing optimizers on a wide range of benchmarks.

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Publication Details

Published
2025-02-06
DOI
https://doi.org/10.1145/3669940.3707240
Citations
6
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
14.89
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article

Optimizing Quantum Circuits, Fast and Slow

Swamit Tannu, Amanda Xu, Abtin Molavi, Aws Albarghouthi
6 citations
Quantum Computing Algorithms and Architecture
14.89
article

Optimizing Quantum Circuits, Fast and Slow

Swamit Tannu, Amanda Xu, Abtin Molavi, Aws Albarghouthi
article en
6 citations

Abstract

Optimizing quantum circuits is critical: the number of quantum operations needs to be minimized for a successful evaluation of a circuit on a quantum processor. In this paper we unify two disparate ideas for optimizing quantum circuits, rewrite rules, which are fast standard optimizer passes, and unitary synthesis, which is slow, requiring a search through the space of circuits. We present a clean, unifying framework for thinking of rewriting and resynthesis as abstract circuit transformations. We then present a radically simple algorithm, guoq, for optimizing quantum circuits that exploits the synergies of rewriting and resynthesis. Our extensive evaluation demonstrates the ability of guoq to strongly outperform existing optimizers on a wide range of benchmarks.

University of Wisconsin–Madison (US)
Affordable and clean energy
Openalex Percentile: Top 1%
Quantum Computing Algorithms and Architecture
14.89
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Optimizing Quantum Circuits, Fast and Slow — Swamit Tannu, Amanda Xu, et al. · (2025) | TGRS Research Map | TGRS