A benchmarking framework that maps quantum computer error rates against arbitrary circuit features and uses Gaussian process regression to build predictive performance models from sparse data.
Evaluating the Q-score of Quantum Annealers
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abstract
We report the Atos Q-score for D-Wave's quantum devices, classical algorithms and hybrid quantum-classical solver. Computing the Q-score entails solving the Max-Cut problem for increasingly large graphs. This work presents the first computation of the Q-score on a quantum device and shows how these quantum devices compare to classical devices at solving optimisation problems. We use D-Wave's standard methods out of the box with a time limit of 60 seconds. The Q-score for D-Wave's 2000Q and Advantage devices are 70 and 140, respectively. The Q-score for two of D-Wave's classical algorithms, based on tabu search and simulated annealing respectively, are 2,300 and 5,800. Finally, we report the out-of-the-box hybrid approach to have a Q-score of 12,500.
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Featuremetric benchmarking: Quantum computer benchmarks based on circuit features
A benchmarking framework that maps quantum computer error rates against arbitrary circuit features and uses Gaussian process regression to build predictive performance models from sparse data.