1London Centre for Nanotechnology, UCL, London WC1H 0AH, UK
2Department of Physics and Astronomy, UCL, London WC1E 6BT, UK
3Department of Electronic & Electrical Engineering, UCL, London WC1E 7JE, UK
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Abstract
We propose a new design heuristic to tackle combinatorial optimisation problems, inspired by Hamiltonians for optimal state-transfer. The result is a rapid approximate optimisation algorithm. We provide numerical evidence of the success of this new design heuristic. We find this approach results in a better approximation ratio than the Quantum Approximate Optimisation Algorithm at lowest depth for the majority of problem instances considered, while utilising comparable resources. This opens the door to investigating new approaches for tackling combinatorial optimisation problems, distinct from adiabatic-influenced approaches.
Popular summary
Quantum algorithms for tackling combinatorial optimisation problems are typically influenced by the adiabatic principle. In short, by going sufficiently slowly it is possible to get from the starting state to the final state. This can result in long run times for the algorithm.
To assess the performance of our new approach we examined its performance on MAX-CUT. We also compared our new approach to the popular Quantum Approximate Optimisation Algorithm (QAOA) in a regime where it utilizes similar resources. Not only did our new approach find better quality solutions, it found them in a shorter time with less classical computational overhead.
Our work opens the door to exploring quantum algorithm design, away from the adiabatic principle, for combinatorial optimisation problems. In the future this new approach might be combined with adiabatic approaches in the development of more sophisticated quantum algorithms.
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Cited by
[1] Boniface Yogendran, Daniel Charlton, Miriam Beddig, Ioannis Kolotouros, and Petros Wallden, “Big data applications on small quantum computers”, arXiv:2402.01529, (2024).
[2] Arthur Braida, Simon Martiel, and Ioan Todinca, “Tight Lieb-Robinson Bound for approximation ratio in Quantum Annealing”, arXiv:2311.12732, (2023).
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