arXiv:2606. 06543v1 Announce Type: cross Abstract: This study examines the coordinated optimization of departure sequencing and section-track allocation in railway short-term concentrated departure scenarios.
arXiv:2607. 24049v1 Announce Type: new Abstract: Short-term disturbances at major passenger railway stations alter train arrival and departure times as well as the release sequence of station resources.
arXiv:2608. 08632v1 Announce Type: new Abstract: Airport landside traffic centers connect terminal arrivals with taxis, ride-hailing vehicles, private cars, buses, metro services, parking facilities, and terminal-area roadways.
arXiv:2606. 14582v1 Announce Type: new Abstract: Efficient route optimization play a vital role in ensuring both safety and punctuality in railway operations.
The paper introduces Adaptive Policy-Guided Error Mitigation (APGEM), a context-aware layer that dynamically selects error mitigation strategies—such as ZNE, PEC, CDR, and REM—during quantum reinforcement learning (QRL) training on NISQ devices. APGEM uses policy-level indicators (quantum-state fidelity, policy entropy, cumulative reward, and approximation ratio) to choose the most suitable mitigation method and integrates it directly into the reinforcement learning loop. Evaluated on the Capacitated Vehicle Routing Problem under various NISQ noise models, APGEM outperforms static mitigation techniques, achieving about 94% of an oracle strategy’s utility, maintaining higher fidelity as noise increases, and producing more stable learning behavior.
The paper presents a method for reducing the size of Quadratic Unconstrained Binary Optimization (QUBO) models used to solve the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) on quantum annealers. It introduces adaptive penalty calibration to improve constraint satisfaction and replaces hand‑tuned merge heuristics with a graph neural network (GNN) that consistently achieves higher feasibility across Solomon benchmark families. Experiments on simulated annealing and a D‑Wave Advantage2 processor show significant reductions in constraint violations and improved feasibility rates, with the QUBO size remaining 5–6 times smaller.
The paper introduces APGEM, an adaptive controller that dynamically selects among four error‑mitigation techniques—Zero‑Noise Extrapolation, Probabilistic Error Cancellation, Clifford Data Regression, and Readout Error Mitigation—based on a utility function and Q‑learning scores. Applied to a realistic Delhi‑based Capacitated Vehicle Routing Problem, the adaptive approach improves the quantum reinforcement learning agent’s approximation ratios from 0.84‑0.87 to 0.92‑0.94 under high noise, outperforming constructive heuristics and approaching metaheuristics. The controller’s strategy shifts from a Clifford‑data‑regression‑heavy regime early in training to a balanced use of all techniques as training progresses, demonstrating regime‑dependent selection.
arXiv:2606. 18503v1 Announce Type: new Abstract: Remaining useful life (RUL) estimation is central to predictive maintenance, where an unplanned failure can cost far more than the asset itself.
arXiv:2607. 09090v1 Announce Type: new Abstract: In large-scale ride-hailing, hold control is a critical mechanism for improving passenger-driver experience.
The paper investigates hybrid quantum‑classical neural networks for learning routing heuristics, focusing on whether small quantum neural networks can replace parameter‑heavy modules in an attention‑based routing model without sacrificing solution quality. For the capacitated vehicle routing problem, replacing the encoder feed‑forward component with a quantum version reduces model parameters by 56.6% while maintaining performance close to the classical baseline on small and medium instances, though the gap widens for larger instances. The study also compares the hybrid approach to classical routing algorithms, finding that classical methods remain highly competitive and often superior on fixed Euclidean test sets, indicating no quantum advantage but highlighting encoder feed‑forward replacement as a viable compression strategy for neural combinatorial optimization.
arXiv:2607. 18774v1 Announce Type: new Abstract: This paper presents an Optuna-based formulation-level auto-tuning framework for support vector machines (SVMs) implemented on multiple quantum-inspired annealers.
arXiv:2609.24042v1 Announce Type: new Abstract: Edge deployment motivates forecasting models with compact parameter storage and low-bit representations. Deep equilibrium models (DEQs) obtain implicit...
arXiv:2606. 27821v1 Announce Type: cross Abstract: Traffic matrices (TMs) capture network-wide origin-destination demand and are central to traffic engineering, yet accurate whole-matrix forecasting remains challenging when prediction must be performed under the memory, update, and training-budget constraints of online network control.