Parliamentary Enquiry - Quantum technologies. Written evidence submitted by Manchester Metropolitan University (QUT0003)

Crispin, AJ (2018) Parliamentary Enquiry - Quantum technologies. Written evidence submitted by Manchester Metropolitan University (QUT0003). UNSPECIFIED. UK Parliament.

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Abstract

Quantum annealing is a search algorithm that can find an overall optimal solution to a problem by leveraging quantum fluctuations in the annealing process. The currently available D-Wave quantum processor uses a hardware array of superconducting flux quantum bits (qubits) for quantum annealing [Johnson et. al. 2011], but research led by Dr Alan Crispin at Manchester Metropolitan University has implemented a quantum annealing simulator that runs on conventional computers. It does not suffer from the same limitation on the number of qubits as is currently available with D-Wave processors. Its algorithm also allows for more flexible problem formulation compared to the Chimera graph architecture used by the D-Wave quantum processor We have shown that our quantum annealing technique, when correctly tuned, can be applied to scheduling and routing problems to improve worker productivity and save on fuel costs, which has economic and environmental benefits. Our research also shows that this approach is particularly suited to solving large-scale optimisation problems Through a Knowledge Transfer Partnership (KTP) with the company ServicePower Technologies Limited, we applied our quantum annealing approach to optimise scheduling for mobile workforces (field service agents). Testing shows that our algorithm finds solutions that are equal to or better than those found through simulated annealing Our algorithm has been successfully patented by the company and is expected to be rolled out into a commercial product for schedule optimisation in due course Significantly, our quantum annealing simulator runs on high specification consumer hardware; it does not suffer from the same limitation on the number of qubits as currently available quantum annealing hardware. Industry, including SMEs, can therefore solve large-scale optimisation problems using a quantum annealing approach today rather than having to wait for R&D to develop and validate new – and likely expensive – hardware solutions The focus of the Industrial Strategy and the Government Office for Science (GoS) report 2016 is on the development of quantum hardware, characterised by large qubit arrays. We argue that this prioritisation neglects the importance of research to simulate tractable quantum mechanical effects (e.g. tunnelling) using consumer class (high performance) workstations or cloud deployments We submit this evidence to highlight the importance of support and funding for further research to study and simulate quantum effects using conventional machinery We recommend the Government encourages more applied research in the field of quantum simulation as we have demonstrated it is “market ready”. We suggest that funding mechanisms such as the KTP scheme would help researchers apply their expertise to real work problems and provide a clear commercial route to integrating quantum software into products.