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Approximate supervised learning of quantum gates via ancillary qubits

Centre for Theoretical Atomic, Molecular, and Optical Physics, School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN, UK

E-mail Address: linnocenti01@qub.ac.uk

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Leonardo Banchi

QOLS, Blackett Laboratory, Imperial College London, London SW7 2AZ, UK

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Sougato Bose

Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK

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Alessandro Ferraro

Centre for Theoretical Atomic, Molecular, and Optical Physics, School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN, UK

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, and

Mauro Paternostro

Centre for Theoretical Atomic, Molecular, and Optical Physics, School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN, UK

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https://doi.org/10.1142/S021974991840004XCited by:0 (Source: Crossref)

This article is part of the issue:

Special Issue on Quantum Techniques in Machine Learning; Guest Editors: Alessandra Di Pierro and Stefano Mancini

Abstract

We present strategies for the training of a qubit network aimed at the ancilla-assisted synthesis of multi-qubit gates based on a set of restricted resources. By assuming the availability of only time-independent single and two-qubit interactions, we introduce and describe a supervised learning strategy implemented through momentum-stochastic gradient descent with automatic differentiation methods. We demonstrate the effectiveness of the scheme by discussing the implementation of nontrivial three qubit operations, including a QFT and a half-adder gate.

Keywords:

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