Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912911134294016 |
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| author | Theißinger, Lukas Gerlach, Thore Berghaus, David Bauckhage, Christian |
| author_facet | Theißinger, Lukas Gerlach, Thore Berghaus, David Bauckhage, Christian |
| contents | Quantum unitary synthesis addresses the problem of translating abstract quantum algorithms into sequences of hardware-executable quantum gates. Solving this task exactly is infeasible in general due to the exponential growth of the underlying combinatorial search space. Existing approaches suffer from misaligned optimization objectives, substantial training costs and limited generalization across different qubit counts. We mitigate these limitations by using supervised learning to approximate the minimum description length of residual unitaries and combining this estimate with stochastic beam search to identify near optimal gate sequences. Our method relies on a lightweight model with zero-shot generalization, substantially reducing training overhead compared to prior baselines. Across multiple benchmarks, we achieve faster wall-clock synthesis times while exceeding state-of-the-art methods in terms of success rate for complex circuits. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15146 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis Theißinger, Lukas Gerlach, Thore Berghaus, David Bauckhage, Christian Quantum Physics Machine Learning Quantum unitary synthesis addresses the problem of translating abstract quantum algorithms into sequences of hardware-executable quantum gates. Solving this task exactly is infeasible in general due to the exponential growth of the underlying combinatorial search space. Existing approaches suffer from misaligned optimization objectives, substantial training costs and limited generalization across different qubit counts. We mitigate these limitations by using supervised learning to approximate the minimum description length of residual unitaries and combining this estimate with stochastic beam search to identify near optimal gate sequences. Our method relies on a lightweight model with zero-shot generalization, substantially reducing training overhead compared to prior baselines. Across multiple benchmarks, we achieve faster wall-clock synthesis times while exceeding state-of-the-art methods in terms of success rate for complex circuits. |
| title | Beyond Reinforcement Learning: Fast and Scalable Quantum Circuit Synthesis |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2602.15146 |