Data di Pubblicazione:
2023
Citazione:
Anomaly detection speed-up by quantum restricted Boltzmann machines / L. Moro, E. Prati. - In: COMMUNICATIONS PHYSICS. - ISSN 2399-3650. - 6:1(2023), pp. 269.1-269.10. [10.1038/s42005-023-01390-y]
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.
Tipologia IRIS:
01 - Articolo su periodico
Elenco autori:
L. Moro, E. Prati
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