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踏上通往量子未来的巴尔的摩轻轨。

On the Baltimore Light RailLink into the quantum future.

作者信息

Domino Krzysztof, Doucet Emery, Robertson Reece, Gardas Bartłomiej, Deffner Sebastian

机构信息

Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Bałtycka 5, 44-100, Gliwice, Poland.

Department of Physics, University of Maryland, Baltimore County, Baltimore, MD, 21250, USA.

出版信息

Sci Rep. 2025 Aug 12;15(1):29576. doi: 10.1038/s41598-025-15545-0.

DOI:10.1038/s41598-025-15545-0
PMID:40796642
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12343960/
Abstract

In the current era of noisy intermediate-scale quantum (NISQ) technology, quantum devices present new avenues for addressing complex, real-world challenges including potentially NP-hard optimization problems. Acknowledging the fact that quantum methods underperform classical solvers, the primary goal of our research is to demonstrate how to leverage quantum noise as a computational resource for optimization. This work aims to showcase how the inherent noise in NISQ devices can be leveraged to solve such real-world problems effectively. Utilizing a D-Wave quantum annealer and IonQ's gate-based NISQ computers, we generate and analyze solutions for managing train traffic under stochastic disturbances. Our case study focuses on the Baltimore Light RailLink, which embodies the characteristics of both tramway and railway networks. We explore the feasibility of using NISQ technology to model the stochastic nature of disruptions in these transportation systems. Our research marks the inaugural application of both quantum computing paradigms to tramway and railway rescheduling, highlighting the potential of quantum noise as a beneficial resource in complex optimization scenarios.

摘要

在当前嘈杂的中尺度量子（NISQ）技术时代，量子设备为应对复杂的现实世界挑战提供了新途径，包括潜在的NP难优化问题。认识到量子方法在性能上不如经典求解器这一事实，我们研究的主要目标是展示如何将量子噪声作为一种计算资源用于优化。这项工作旨在展示如何利用NISQ设备中的固有噪声来有效解决此类现实世界问题。我们利用一台D-Wave量子退火器和IonQ基于门的NISQ计算机，生成并分析了在随机干扰下管理列车交通的解决方案。我们的案例研究聚焦于巴尔的摩轻轨，它兼具有轨电车网络和铁路网络的特征。我们探讨了使用NISQ技术对这些运输系统中断的随机性质进行建模的可行性。我们的研究标志着这两种量子计算范式首次应用于有轨电车和铁路重新调度，凸显了量子噪声在复杂优化场景中作为有益资源的潜力。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/86de/12343960/e9eaadb043d2/41598_2025_15545_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/86de/12343960/26d2430d6b0d/41598_2025_15545_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/86de/12343960/7c7391a29060/41598_2025_15545_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/86de/12343960/a2523bd6636c/41598_2025_15545_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/86de/12343960/79a5753e47e1/41598_2025_15545_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/86de/12343960/2325b14fdd11/41598_2025_15545_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/86de/12343960/92dd70bae4da/41598_2025_15545_Fig11_HTML.jpg
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Hybrid quantum-classical computation for automatic guided vehicles scheduling.用于自动导引车调度的混合量子-经典计算
Sci Rep. 2024 Sep 18;14(1):21809. doi: 10.1038/s41598-024-72101-y.
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Robustness and disturbances in public transport.公共交通的稳健性与干扰因素
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Sci Adv. 2024 Mar 15;10(11):eadj5170. doi: 10.1126/sciadv.adj5170.
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Quantum Annealing in the NISQ Era: Railway Conflict Management.含噪声中等规模量子（NISQ）时代的量子退火：铁路冲突管理
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Training Variational Quantum Algorithms Is NP-Hard.训练变分量子算法是NP难问题。
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