Approximate supervised learning of quantum gates via ancillary qubits

Luca Innocenti, Leonardo Banchi, Sougato Bose, Alessandro Ferraro, Mauro Paternostro

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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.

Original languageEnglish
Article number1840004
JournalInternational Journal of Quantum Information
Volume16
Issue number8
DOIs
Publication statusPublished - 01 Dec 2018

Keywords

  • quantum-computation
  • quantum-gates
  • Quantum-information

ASJC Scopus subject areas

  • Physics and Astronomy (miscellaneous)

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  • Student Theses

    Machine-learning-assisted state and gate engineering for quantum technologies

    Author: Innocenti, L., Dec 2020

    Supervisor: Ferraro, A. (Supervisor) & Paternostro, M. (Supervisor)

    Student thesis: Doctoral ThesisDoctor of Philosophy

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