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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.03123 |
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| _version_ | 1866915711269470208 |
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| author | Gomathi, Janani Meiburg, Alex |
| author_facet | Gomathi, Janani Meiburg, Alex |
| contents | When the gate set has continuous parameters, synthesizing a unitary operator as a quantum circuit is always possible using exact methods, but finding minimal circuits efficiently remains a challenging problem. The landscape is very different for compiled unitaries, which arise from programming and typically have short circuits, as compared with generic unitaries, which use all parameters and typically require circuits of maximal size. We show that simple gradient descent reliably finds depth- and gate-optimal circuits for generic unitaries, including in the presence of restricted chip connectivity. This runs counter to earlier evidence that optimal synthesis required combinatorial search, and we show that this discrepancy can be explained by avoiding the random selection of certain parameter-deficient circuit skeletons. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03123 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Gradient descent reliably finds depth- and gate-optimal circuits for generic unitaries Gomathi, Janani Meiburg, Alex Quantum Physics Machine Learning 81P68 When the gate set has continuous parameters, synthesizing a unitary operator as a quantum circuit is always possible using exact methods, but finding minimal circuits efficiently remains a challenging problem. The landscape is very different for compiled unitaries, which arise from programming and typically have short circuits, as compared with generic unitaries, which use all parameters and typically require circuits of maximal size. We show that simple gradient descent reliably finds depth- and gate-optimal circuits for generic unitaries, including in the presence of restricted chip connectivity. This runs counter to earlier evidence that optimal synthesis required combinatorial search, and we show that this discrepancy can be explained by avoiding the random selection of certain parameter-deficient circuit skeletons. |
| title | Gradient descent reliably finds depth- and gate-optimal circuits for generic unitaries |
| topic | Quantum Physics Machine Learning 81P68 |
| url | https://arxiv.org/abs/2601.03123 |