Quantum Annealing for Transport Optimization - QATO

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.