Fair sampling with temperature-targeted QAOA based on quantum-classical correspondence theory
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866915747854286848 |
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| author | Abe, Tetsuro Tanaka, Shu |
| author_facet | Abe, Tetsuro Tanaka, Shu |
| contents | In combinatorial optimization problems with degenerate ground states, fair sampling of degenerate solutions is essential. However, the quantum approximate optimization algorithm (QAOA) with a standard transverse-field mixer induces biases among degenerate states as circuit depth increases. Based on quantum-classical correspondence theory, we propose SBO-QAOA, which employs a temperature-dependent Hamiltonian encoding a Gibbs distribution as its ground state. Numerical simulations show that, unlike standard QAOA, SBO-QAOA yields ground-state probabilities converging to finite-temperature values with uniform distribution among degenerate states. These fairness and temperature-targeting properties are preserved even with only four variational parameters under a linear schedule. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16144 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fair sampling with temperature-targeted QAOA based on quantum-classical correspondence theory Abe, Tetsuro Tanaka, Shu Quantum Physics Statistical Mechanics In combinatorial optimization problems with degenerate ground states, fair sampling of degenerate solutions is essential. However, the quantum approximate optimization algorithm (QAOA) with a standard transverse-field mixer induces biases among degenerate states as circuit depth increases. Based on quantum-classical correspondence theory, we propose SBO-QAOA, which employs a temperature-dependent Hamiltonian encoding a Gibbs distribution as its ground state. Numerical simulations show that, unlike standard QAOA, SBO-QAOA yields ground-state probabilities converging to finite-temperature values with uniform distribution among degenerate states. These fairness and temperature-targeting properties are preserved even with only four variational parameters under a linear schedule. |
| title | Fair sampling with temperature-targeted QAOA based on quantum-classical correspondence theory |
| topic | Quantum Physics Statistical Mechanics |
| url | https://arxiv.org/abs/2601.16144 |