Solving rescheduling problems in heterogeneous urban railway networks using hybrid quantum-classical approach

• URL: http://arxiv.org/abs/2309.06763v6
• Date: Tue, 11 Feb 2025 09:01:32 GMT
• Title: Solving rescheduling problems in heterogeneous urban railway networks using hybrid quantum-classical approach
• Authors: Mátyás Koniorczyk, Krzysztof Krawiec, Ludmila Botelho, Nikola Bešinović, Krzysztof Domino,
• Abstract summary: We build an integer linear programming model and solve it with D-Wave's quantum-classical hybrid solver (CQM) The proposed approach is demonstrated on a real-life heterogeneous urban network in Poland.
• Score: 0.157286095422595
• License:
• Abstract: We address the applicability of a hybrid quantum-classical heuristics for practical railway rescheduling management problems. We build an integer linear programming model and solve it with D-Wave's quantum-classical hybrid solver (CQM) as well as with CPLEX, for comparison. The proposed approach is demonstrated on a real-life heterogeneous urban network in Poland, including both single- and multi-track segments. All the requirements posed by the operator of the network included in the model. The computational results demonstrate the readiness for application and the benefits of quantum-classical hybrid solvers in a realistic railway scenario: they yield acceptable solutions on time, which is a critical requirement in a rescheduling situation. In particular, CQM as a probabilistic heuristic solver provides a number of feasible, close-to-optimal solutions the dispatcher can choose from.

Related papers

• A hybrid Quantum-Classical Algorithm for Mixed-Integer Optimization in Power Systems [0.0]
  We present a framework for solving power system optimization problems with a Quantum Computer (QC) Our guiding applications are the optimal transmission switching and the verification of neural networks trained to solve a DC Optimal Power Flow.
  arXiv  Detail & Related papers  (2024-04-16T16:11:56Z)
• A Hybrid Quantum-Classical Approach to the Electric Mobility Problem [0.8796261172196743]
  We suggest a hybrid quantum-classical routine for the NP-hard Electric Vehicle Fleet Charging and Allocation Problem. We benchmark the performance of the decomposition technique with classical and quantum-inspired metaheuristics. The major advantage of the proposed approach is that it enables quantum-based methods for this realistic problem with many inequality constraints.
  arXiv  Detail & Related papers  (2023-10-04T12:14:56Z)
• Hybrid quantum-classical computation for automatic guided vehicles scheduling [0.0]
  We demonstrate the effectiveness of state-of-the-art hybrid (not necessarily quantum) solvers in addressing the business-centric problem of scheduling Automatic Guided Vehicles. We utilize D-Wave hybrid solvers that implement classicals with potential assistance from a quantum processing unit. We show that a scenario involving up to 21 AGVs, significant due to possible deadlocks, can be efficiently addressed by a hybrid solver in seconds.
  arXiv  Detail & Related papers  (2023-09-01T09:11:53Z)
• Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud Computing [73.7522199491117]
  Quantum cloud computing (QCC) offers a promising approach to efficiently provide quantum computing resources. The fluctuations in user demand and quantum circuit requirements are challenging for efficient resource provisioning. We propose a resource allocation model to provision quantum computing and networking resources.
  arXiv  Detail & Related papers  (2023-07-25T00:38:46Z)
• Quantum-inspired optimization for wavelength assignment [51.55491037321065]
  We propose and develop a quantum-inspired algorithm for solving the wavelength assignment problem. Our results pave the way to the use of quantum-inspired algorithms for practical problems in telecommunications.
  arXiv  Detail & Related papers  (2022-11-01T07:52:47Z)
• Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms [0.0]
  We solve robot trajectory planning problems at industry-relevant scales. Our end-to-end solution integrates highly versatile random-key algorithms with model stacking and ensemble techniques. We show how the latter can be integrated into our larger pipeline, providing a quantum-ready hybrid solution to the problem.
  arXiv  Detail & Related papers  (2022-06-08T02:38:32Z)
• Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
  We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem. The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment. We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
  arXiv  Detail & Related papers  (2022-04-18T21:45:13Z)
• A Hybrid Quantum-Classical Algorithm for Robust Fitting [47.42391857319388]
  We propose a hybrid quantum-classical algorithm for robust fitting. Our core contribution is a novel robust fitting formulation that solves a sequence of integer programs. We present results obtained using an actual quantum computer.
  arXiv  Detail & Related papers  (2022-01-25T05:59:24Z)
• Error mitigation and quantum-assisted simulation in the error corrected regime [77.34726150561087]
  A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations. We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
  arXiv  Detail & Related papers  (2021-03-12T20:58:41Z)
• Quantum computing approach to railway dispatching and conflict management optimization on single-track railway lines [0.4724825031148411]
  We consider a practical railway dispatching problem: delay and conflict management on a single-track railway line. We introduce a quadratic unconstrained binary optimization (QUBO) model of the problem in question, compatible with the emerging quantum annealing technology. As a proof-of-concept, we solve selected real-life problems from the Polish railway network using D-Wave quantum annealers.
  arXiv  Detail & Related papers  (2020-10-16T08:17:57Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.