Quantum Physics using Weighted Model Counting
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915961571901440 |
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| author | Ende, Dirck van den Lee, Joon Hyung Laarman, Alfons Basold, Henning |
| author_facet | Ende, Dirck van den Lee, Joon Hyung Laarman, Alfons Basold, Henning |
| contents | Weighted model counting (WMC) has proven effective at a range of tasks within computer science, physics, and beyond. However, existing approaches for using WMC in quantum physics only target specific problem instances, lacking a general framework for expressing problems using WMC. This limits the reusability of these approaches in other applications and risks a lack of mathematical rigor on a per-instance basis. We present an approach for expressing linear algebraic problems, specifically those present in physics and quantum computing, as WMC instances. We do this by introducing a framework that converts Dirac notation to WMC problems. We build up this framework theoretically, using a type system and denotational semantics, and provide an implementation in Python. We demonstrate the effectiveness of our framework in calculating the partition functions of several physical models: The transverse-field Ising model (quantum) and the Potts model (classical). The results suggest that heuristics developed in automated reasoning can be systematically applied to a wide class of problems in quantum physics through our framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_21288 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Physics using Weighted Model Counting Ende, Dirck van den Lee, Joon Hyung Laarman, Alfons Basold, Henning Quantum Physics Statistical Mechanics Mathematical Physics 82B44, 68Q17, 81P68 Weighted model counting (WMC) has proven effective at a range of tasks within computer science, physics, and beyond. However, existing approaches for using WMC in quantum physics only target specific problem instances, lacking a general framework for expressing problems using WMC. This limits the reusability of these approaches in other applications and risks a lack of mathematical rigor on a per-instance basis. We present an approach for expressing linear algebraic problems, specifically those present in physics and quantum computing, as WMC instances. We do this by introducing a framework that converts Dirac notation to WMC problems. We build up this framework theoretically, using a type system and denotational semantics, and provide an implementation in Python. We demonstrate the effectiveness of our framework in calculating the partition functions of several physical models: The transverse-field Ising model (quantum) and the Potts model (classical). The results suggest that heuristics developed in automated reasoning can be systematically applied to a wide class of problems in quantum physics through our framework. |
| title | Quantum Physics using Weighted Model Counting |
| topic | Quantum Physics Statistical Mechanics Mathematical Physics 82B44, 68Q17, 81P68 |
| url | https://arxiv.org/abs/2508.21288 |