arXiv AI June 8, 2026 By Xiaobin Li, Yanbin Gao, Weiguang Wang, Xuechen Liang

Coordinated optimization of departure sequencing and section-track allocation in railway short-term concentrated departure scenarios based on qubo and hybrid quantum algorithms

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 28

Quantum-Inspired Evolutionary Neighborhood Search for Arrival-Departure Track Utilization Adjustment under Short-Term Disturbances

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.

By Xiaobin Li, Wuming Lei, Yanbin Gao, Weiguang Wang

arXiv AI

Aug 11

A QUBO-Inspired Computational Framework for Airport Landside Bottleneck Diagnosis and Dynamic Dispatch Optimization

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.

By Wuming Lei, Xiaobin Li, Mingyan Sun, Jianing Long, Yulin Tong, Yanbin Gao

arXiv AI

Jun 15

A Temporal Planning Framework for Disruption Aware Dynamic Route Optimization in Heterogeneous Railway Systems

arXiv:2606. 14582v1 Announce Type: new Abstract: Efficient route optimization play a vital role in ensuring both safety and punctuality in railway operations.

By Pollob Chandra Ray, Sabah Binte Noor, Fazlul Hasan Siddiqui

benchmarks safety

arXiv Machine Learning

Jun 18

Quantum Annealing Enhanced Reinforcement Learning for Accurate Remaining Useful Lifetime Prediction

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.

By Manoranjan Gandhudi, Arunkumar V., G. R. Anil, Gangadharan G. R

rag reinforcement-learning benchmarks

arXiv Machine Learning

Jul 13

EXHOLD: Experience-Aware Real-Time Hold Control for Large-Scale Ride-Hailing Matching at DiDi

arXiv:2607. 09090v1 Announce Type: new Abstract: In large-scale ride-hailing, hold control is a critical mechanism for improving passenger-driver experience.

By Xu Liu, Kai Wan, Zihao Lu

safety

arXiv Machine Learning

Jul 22

Formulation-Level Auto-Tuning for QUBO-Based Machine Learning: A Case Study Across Multiple Quantum-Inspired Annealers

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.

By Naoya Mizuki, Takahiro Katagiri, Daichi Mukunoki, Tetsuya Hoshino

Related stories

Quantum-Inspired Evolutionary Neighborhood Search for Arrival-Departure Track Utilization Adjustment under Short-Term Disturbances

A QUBO-Inspired Computational Framework for Airport Landside Bottleneck Diagnosis and Dynamic Dispatch Optimization

A Temporal Planning Framework for Disruption Aware Dynamic Route Optimization in Heterogeneous Railway Systems

Quantum Annealing Enhanced Reinforcement Learning for Accurate Remaining Useful Lifetime Prediction

EXHOLD: Experience-Aware Real-Time Hold Control for Large-Scale Ride-Hailing Matching at DiDi

Formulation-Level Auto-Tuning for QUBO-Based Machine Learning: A Case Study Across Multiple Quantum-Inspired Annealers