Fair sampling with temperature-targeted QAOA based on quantum-classical correspondence theory

• URL: http://arxiv.org/abs/2601.16144v1
• Date: Thu, 22 Jan 2026 17:36:32 GMT
• Title: Fair sampling with temperature-targeted QAOA based on quantum-classical correspondence theory
• Authors: Tetsuro Abe, Shu Tanaka,
• Abstract summary: We propose SBO-QAOA, which employs a temperature-dependent Hamiltonian encoding a Gibbs distribution as its ground state.<n> Numerical simulations show that, unlike standard QAOA, SBO-QAOA yields ground-state probabilities converging to finite-temperature values with uniform distribution among degenerate states.
• Score: 0.7519872646378835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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