Optimizing Quantum Circuits, Fast and Slow
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866916470510845952 |
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| author | Xu, Amanda Molavi, Abtin Tannu, Swamit Albarghouthi, Aws |
| author_facet | Xu, Amanda Molavi, Abtin Tannu, Swamit Albarghouthi, Aws |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_04104 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing Quantum Circuits, Fast and Slow Xu, Amanda Molavi, Abtin Tannu, Swamit Albarghouthi, Aws Programming Languages Quantum Physics 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. |
| title | Optimizing Quantum Circuits, Fast and Slow |
| topic | Programming Languages Quantum Physics |
| url | https://arxiv.org/abs/2411.04104 |