Quantum Annealing for Transport Optimization - QATO

量子退火运输优化（QATO）是一个研究项目，探索量子在智能城市智能移动解决方案中的实际应用。它使用CGA为大曼彻斯特运输模拟开发的物流规划工具，哈特里中心将提供先进的计算设施，用于开发可以在D-Wave量子退火机上运行的软件。该项目旨在开发量子退火的公式，评估混合经典-量子解决方案，并分析用量子计算机解决这些工业问题的硬件和软件要求。该项目还旨在展示将量子计算用于预测分析的商业效益。通过准确预测结果，决策者可以节省交付时间，从而产生巨大的价值。该项目旨在使用量子退火来解决与最后一英里配送物流和移动即服务相关的复杂优化问题，传统的优化方法在计算上昂贵且耗时。这是为了解决大曼彻斯特交通局在运输工业部门确定的需求，与哈特里中心的磋商表明，这将是量子计算的合适用例。Hartree可以将量子计算方法应用于CGA Simulation开发的一套现有软件解决方案，以提高经典优化方法（如Agent Based modeling）提供的预测准确性。在项目过去的研究和开发周期中，与利物浦城市地区联合管理局、交通研究实验室和行业等利益相关者的磋商表明，商业需要更好的工具来模拟物流和旅行新方法对未来交通模式的影响。证明量子在预测精度方面的优势具有明确的工业应用前景。如果决策者安装基础设施可以准确预测结果，他们就可以节省交付时间。个人旅行的小额储蓄累积起来就是巨大的利润。对于工业中观察到的问题，我们希望开发量子退火的公式，评估混合经典量子解决方案，并分析用量子计算机解决这些工业问题的硬件和软件要求。Hartree打算用模拟量子退火来测试Atos量子学习机（QLM），以开发一种可以在D-Wave量子退火机上运行的软件方法。该问题的表述将适用于基于门的量子计算机和量子退火。该团队打算评估量子退火，以支持不同技术的发展，与基于门的量子计算的现有努力相辅相成。

Quantum Annealing for Transport Optimisation (QATO) is a research project exploring the practical use of quantum for smart mobility solutions for smart cities. It uses a logistics planning tool developed by CGA Simulation for Transport for Greater Manchester, and the Hartree Centre will provide advanced computing facilities for developing software that can run on a D-Wave quantum annealer. The project aims to develop a formulation for quantum annealing, evaluate hybrid classical-quantum solutions, and analyse the hardware and software requirements for solving these industrial problems with quantum computers. The project also aims to demonstrate the commercial benefits of using quantum computing for predictive analytics. By accurately predicting outcomes, decision-makers can save time on deliveries, resulting in significant value.The project aims to use quantum annealing to solve complex optimization problems related to last mile delivery logistics and Mobility as a Service, which classical optimization methods find computationally expensive and time-consuming. This is addressing a need identified by Transport for Greater Manchester in the transport industrial sector that consultation with the Hartree Centre suggests would be a suitable use-case for Quantum Computing. Hartree can apply Quantum Computing approaches to an existing set of software solutions developed by CGA Simulation to increase the accuracy of predication provided by classical optimisation methods such as Agent Based Modelling.During past Research and Development cycles on the project, consultation with Stakeholders such as Liverpool City Region Combined Authority, the Transport Research Laboratory and industry indicates a commercial need for better tools to simulate the impact of new approaches to logistics and travel on future transport patterns.Demonstrating the advantage of quantum in predictive accuracy has a clear industrial application. If decision makers installing infrastructure can accurately predict outcomes they can save time on deliveries,. Small savings on individual journeys add up to vast profits.For problems observed in industry we want to develop a formulation for quantum annealing and evaluate hybrid classical quantum solutions and analyse the hardware and software requirements for solving these industrial problems with quantum computers.Hartree intends to test the Atos Quantum Learning Machine (QLM), with simulated quantum annealing, to develop a software approach that can run on a D-Wave quantum annealer. The problem formulation will be valid for both gate-based quantum computers and quantum annealing. The team intends to evaluate quantum annealing to support the development of different technologies complementary to existing efforts in gate-based quantum computing.