{"ID":2828066,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.15476","arxiv_id":"2512.15476","title":"QuantGraph: A Receding-Horizon Quantum Graph Solver","abstract":"Dynamic programming is a cornerstone of graph-based optimization. While effective, it scales unfavorably with problem size. In this work, we present QuantGraph, a two-stage quantum-enhanced framework that casts local and global graph-optimization problems as quantum searches over discrete trajectory spaces. The solver is designed to operate efficiently by first finding a sequence of locally optimal transitions in the graph (local stage), without considering full trajectories. The accumulated cost of these transitions acts as a threshold that prunes the search space (up to 60% reduction for certain examples). The subsequent global stage, based on this threshold, refines the solution. Both stages utilize variants of the Grover-adaptive-search algorithm. To achieve scalability and robustness, we draw on principles from control theory and embed QuantGraph's global stage within a receding-horizon model-predictive-control scheme. This classical layer stabilizes and guides the quantum search, improving precision and reducing computational burden. In practice, the resulting closed-loop system exhibits robust behavior and lower overall complexity. Notably, for a fixed query budget, QuantGraph attains a 2x increase in control-discretization precision while still benefiting from Grover-search's inherent quadratic speedup compared to classical methods.","short_abstract":"Dynamic programming is a cornerstone of graph-based optimization. While effective, it scales unfavorably with problem size. In this work, we present QuantGraph, a two-stage quantum-enhanced framework that casts local and global graph-optimization problems as quantum searches over discrete trajectory spaces. The solver...","url_abs":"https://arxiv.org/abs/2512.15476","url_pdf":"https://arxiv.org/pdf/2512.15476v1","authors":"[\"Pranav Vaidhyanathan\",\"Aristotelis Papatheodorou\",\"David R. M. Arvidsson-Shukur\",\"Mark T. Mitchison\",\"Natalia Ares\",\"Ioannis Havoutis\"]","published":"2025-12-17T14:22:08Z","proceeding":"quant-ph","tasks":"[\"quant-ph\",\"cs.RO\",\"eess.SY\",\"physics.comp-ph\"]","methods":"[]","has_code":false}