Resource-Scalable Fully Quantum Metropolis-Hastings for Integer Linear Programming

• URL: http://arxiv.org/abs/2602.11285v1
• Date: Wed, 11 Feb 2026 19:01:45 GMT
• Title: Resource-Scalable Fully Quantum Metropolis-Hastings for Integer Linear Programming
• Authors: Gabriel Escrig, Roberto Campos, M. A. Martin-Delgado,
• Abstract summary: We introduce a fully quantum Metropolis-Hastings algorithm for Metropolis linear programming.<n>Each walk step is a unitary update that prepares coherent candidate moves.<n>We show that the method provides a coherent, resource-characterized baseline for fully quantum constraint programming.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Integer linear programming (ILP) remains computationally challenging due to its NP-complete nature despite its central role in scheduling, logistics, and design optimization. We introduce a fully quantum Metropolis-Hastings algorithm for ILP that implements a coherent random walk over the discrete feasible region using only reversible quantum circuits, without quantum-RAM assumptions or classical pre/post-processing. Each walk step is a unitary update that prepares coherent candidate moves, evaluates the objective and constraints reversibly -- including a constraint-satisfaction counter to enforce feasibility -- and encodes Metropolis acceptance amplitudes via a low-overhead linearized rule. At the logical level, the construction uses $\mathcal{O}(n\log_2 N)$ qubits to represent $n$ integer variables over the interval $[-N,\,N-1]$, and the Toffoli-equivalent cost per Metropolis step grows linearly with the total logical qubit count. Using explicit ripple-carry adder constructions, we support linear objectives and mixed equality/inequality constraints. Numerical circuit-level simulations on a broad ensemble of randomly generated instances validate the predicted linear resource scaling and exhibit progressive thermalization toward low-cost feasible solutions under the annealing schedule. Overall, the method provides a coherent, resource-characterized baseline for fully quantum constraint programming and a foundation for incorporating additional quantum speedups in combinatorial optimization.

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