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• W4386982579 abstract "Abstract Quantum machine learning promises to revolutionize traditional machine learning by efficiently addressing hard tasks for classical computation. While claims of quantum speed-up have been announced for gate-based quantum computers and photon-based boson samplers, demonstration of an advantage by adiabatic quantum annealers (AQAs) is open. Here we quantify the computational cost and the performance of restricted Boltzmann machines (RBMs), a widely investigated machine learning model, by classical and quantum annealing. Despite the lower computational complexity of the quantum RBM being lost due to physical implementation overheads, a quantum speed-up may arise as a reduction by orders of magnitude of the computational time. By employing real-world cybersecurity datasets, we observe that the negative phase on sufficiently challenging tasks is computed up to 64 times faster by AQAs during the exploitation phase. Therefore, although a quantum speed-up highly depends on the problem’s characteristics, it emerges in existing hardware on real-world data." @default.
• W4386982579 created "2023-09-24" @default.
• W4386982579 creator A5017727346 @default.
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• W4386982579 date "2023-09-23" @default.
• W4386982579 modified "2023-10-18" @default.
• W4386982579 title "Anomaly detection speed-up by quantum restricted Boltzmann machines" @default.
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• W4386982579 doi "https://doi.org/10.1038/s42005-023-01390-y" @default.
• W4386982579 hasPublicationYear "2023" @default.
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