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arXiv:1810.09342 · Quantum Information Processing

Quantum annealing learning search for solving QUBO problems

Davide Pastorello, Enrico Blanzieri

Published Aug 20, 2019• 36 citations
arXivHTMLPDF
DOI
10.1007/s11128-019-2418-z

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Quantum computers are early-stage technology, but are progressing rapidly. Here, we compare metaheuristics with a quantum annealer (QA), a quantum computer tailored to solve combinatorial optimisation problems formulated as a Quadratic Unconstrained Binary Optimisation (QUBO) problem. Formulating non-trivial optimisation problems as QUBOs is a challenge, so it is to ensure that the direct comparison of classical and quantum search algorithms is conducted fairly. This paper illustrates the use of a streamlined methodology to derive a QUBO for a classical optimisation problem – the Steiner tree problem – and provides comprehensive experiments comparing D-WAVE pure QA and hybrid QA with classical methods including Simulated Annealing and Genetic Algorithms. We find that the pure QA suffers from considerable qubit noise but the hybrid QA is on a par with traditional methods.
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