Chronos-QRL: Outperforming Google OR-Tools, Attention Model, and POMO on the International Dynamic VRPTW Benchmark via 28-Qubit Floquet-MBL Superposition in 4GB VRAM

The Dynamic Vehicle Routing Problem with Time Windows (Dynamic VRPTW) constitutes the most demanding international standard for evaluating autonomous dispatch algorithms for massive fleets under combinatorial uncertainty O(2^N). Classical local search metaheuristics (Google OR-Tools GLS and HGS-VRP) suffer an exponential growth in computation time when re-optimizing routes live, while state-of-the-art autoregressive deep models (Attention Model – AM, ICLR 2019; and POMO, NeurIPS 2020) are confined to the TC0 circuit class, collapsing under correlated congestion cascades of order k ≥ 6. In this work, we present Chronos-QRL, a Quantum Reinforcement Learning agent that maps the topological and temporal state of the Solomon RC101 graph (100 clients + central depot, Q = 200, Tmax = 240) onto an N = 28 qubit Floquet-MBL Time Crystal (268,435,456 states in 3.221 GB of VRAM) coupled with a Quantum Natural Policy Gradient optimizer (Fubini-Study Metric QNPG + SPSA) with zero Backpropagation. Audited over 16,000 dynamic epochs at a speed of 5,940,668 decisions/second (2.693 ms on a 4GB VRAM RTX 3050 Ti GPU), Chronos-QRL unsupervisedly discovers the critical 6-node subgraph, raises the bottleneck detection ROC-AUC from 0.5121 (AM/POMO collapse) to 0.9985 (+48.64 pts), and reduces the dynamic optimality gap relative to the Best Known Solutions (BKS) from +39.41% (Solomon I1), +23.01% (OR-Tools), and +20.55% (POMO) down to just +0.83% (95.6% BKS hit rate).