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Cambridge Quantum and Deutsche Bahn Netz AG Leverage Latest Quantum Algorithms to Optimise Train Scheduling
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News provided by

Cambridge Quantum

11 Nov, 2021, 13:30 IST

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CAMBRIDGE, England, Nov. 11, 2021 /PRNewswire/ -- Cambridge Quantum (CQ) and Deutsche Bahn Netz AG (DB) announce today a partnership to explore how quantum computers can improve the rescheduling of rail traffic as part of DB's  long-term transformative plan, Digitale Schiene Deutschland, to digitise DB's infrastructure and railway system using next-generation technologies to achieve a higher capacity and optimal utilisation of the rail network.

Combining CQ's latest combinatorial optimisation algorithm Filtering Variational Quantum Eigensolver (F-VQE) - recently shown to outperform leading quantum algorithms - with DB's operations research expertise, the team re-optimised realistic train timetables after simulated delays and are now identifying areas for continued study. This collaboration evidences how innovations in both quantum algorithms and domain-specific modelling can inform a long-term vision for a faster and greener transportation network.

Ilyas Khan, CEO of Cambridge Quantum, said, "We are very excited to be working with Deutsche Bahn to explore and demonstrate the utility of today's Noisy Intermediate Scale Quantum ("NISQ") processors to solve real-world problems in the transport and logistics sector. Deutsche Bahn's research and development efforts in this area are of critical importance, and we are confident that over time as quantum computers start to scale, our work with the will lead to a meaningful contribution towards a cleaner and greener future."

Michael Küpper, lead of Capacity and Traffic Management System at Digitale Schiene Deutschland, said, "The collaboration with CQ is a perfect example of how Deutsche Bahn is working as a partner with industry providers and combining our relative expertise towards a goal neither side can achieve alone. By working with CQ, we have fine-tuned our research and development plans and taken the first steps in defining a future quantum-advantaged train timetabling system. We are excited to continue working with CQ to address some of the key challenges and contribute to the rapidly evolving field of NISQ quantum algorithm research."

About DB

As a subsidiary of Deutsche Bahn AG, DB Netz AG is responsible for the rail infrastructure.

DB Netz AG is the service provider for currently 420 railway undertakings (RUs) utilising a route network comprising nearly 33,300 km. The German railway network is the longest in all of Europe. DB Netz AG ensures non-discriminatory access to its infrastructure. The performance of non-DB-Group railways in the network has greatly increased over the years. DB Netz is responsible for the operation of an efficient railway infrastructure (long-distance and conurbation networks, regional networks, train-formation and treatment facilities).

Digitale Schiene Deutschland is a sector initiative of DB AG, the German Federal Ministry of Transport and Digital Infrastructure (BMVI) and further relevant transport associations for the fundamental modernisation and digitalisation of railway infrastructure through the consistent introduction of digital control and safety technology. In addition, Digitale Schiene Deutschland is working on a far-reaching digitalisation of the railway system. For this, a system architecture will detail the tasks of individual components of the railway system, and how they should work together.

About CQ

Founded in 2014 and backed by some of the world's leading quantum computing companies, CQ is a global leader in quantum software and quantum algorithms, enabling clients to achieve the most out of rapidly evolving quantum computing hardware. CQ has offices in Europe, USA, and Japan. On 8th June 2021, CQ announced a merger with Honeywell Quantum Solutions which is expected to close in Q4 2021. For more information, visit CQ at http://www.cambridgequantum.com and on LinkedIn.

Access the source code for lambeq, TKET, Python bindings and utilities on GitHub.

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