Supervised learning of random quantum circuits via scalable neural networks

Year: 2023

Authors: Cantori S., Vitali D., Pilati S.

Autors Affiliation: Univ Camerino, Sch Sci & Technol, Phys Div, I-62032 Camerino, MC, Italy; INFN Sez Perugia, I-06123 Perugia, Italy; CNR INO, I-50125 Florence, Italy.

Abstract: Predicting the output of quantum circuits is a hard computational task that plays a pivotal role in the development of universal quantum computers. Here we investigate the supervised learning of output expectation values of random quantum circuits. Deep convolutional neural networks (CNNs) are trained to predict single-qubit and two-qubit expectation values using databases of classically simulated circuits. These circuits are built using either a universal gate set or a continuous set of rotations plus an entangling gate, and they are represented via properly designed encodings of these gates. The prediction accuracy for previously unseen circuits is analyzed, also making comparisons with small-scale quantum computers available from the free IBM Quantum program. The CNNs often outperform these quantum devices, depending on the circuit depth, on the network depth, and on the training set size. Notably, our CNNs are designed to be scalable. This allows us exploiting transfer learning and performing extrapolations to circuits larger than those included in the training set. These CNNs also demonstrate remarkable resilience against noise, namely, they remain accurate even when trained on (simulated) expectation values averaged over very few measurements.

Journal/Review: QUANTUM SCIENCE AND TECHNOLOGY

Volume: 8 (2)      Pages from: 25022-1  to: 25022-20

More Information: This work was supported by the Italian Ministry of University and Research under the PRIN2017 Project CEnTraL 20172H2SC4, and by the European Union Horizon 2020 Programme for Research and Innovation through the Project No. 862644 (FET Open QUARTET). S P acknowledges PRACE for awarding access to the Fenix Infrastructure resources at Cineca, which are partially funded by the European Union’s Horizon 2020 research and innovation program through the ICEI project under the Grant Agreement No. 800858. S Cantori acknowledges partial support from the B-GREEN project of the italian MiSE-Bando 2018 Industria Sostenibile. This work was also supported by the PNRR MUR Project PE0000023-NQSTI. The authors acknowledge the use of IBM Quantum services for this work. The views expressed are those of the authors, and do not reflect the official policy or position of IBM or the IBM Quantum team.
KeyWords: random quantum circuits; quantum computing; deep learning
DOI: 10.1088/2058-9565/acc4e2

Citations: 4
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