Speaker
Sebastiano Pilati
(School of Science and Technology, Physics Division, University of Camerino, 62032 Camerino, Italy)
Description
We investigate the combined use of quantum computers and classical deep neural networks, considering both quantum annealers and universal gate-based platforms.
In the first case, we show that data produced by D-Wave quantum annealers allow accelerating Monte Carlo simulations of spin glasses through the training of autoregressive neural networks [1].
In the second case, we show that deep neural networks fed with a combination of data from noisy quantum computers and classical circuit descriptors are able to emulate otherwise classically intractable quantum circuits, thus also achieving an effective error mitigation scheme [2].
- G. Scriva, E. Costa, B. McNaughton, S. Pilati, “Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning”, SciPost Physics 15, 018 (2023).
- S. Cantori, A. Mari, D. Vitali, S. Pilati, “Synergy between noisy quantum computers and scalable classical deep learning”, EPJ Quantum Technology 11, 45 (2024).
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Primary authors
Andrea Mari
(School of Science and Technology, Physics Division, University of Camerino, 62032 Camerino, Italy)
David Vitali
(Istituto Nazionale di Fisica Nucleare)
Emanuele Costa
(University of Barcelona)
Giuseppe Scriva
(School of Science and Technology, Physics Division, University of Camerino, 62032 Camerino, Italy)
Sebastiano Pilati
(School of Science and Technology, Physics Division, University of Camerino, 62032 Camerino, Italy)
Simone Cantori
(School of Science and Technology, Physics Division, Università di Camerino, 62032 Camerino (MC), Italy)
