Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning

Author:

Preti Francesco12,Schilling Michael12,Jerbi Sofiene34,Trenkwalder Lea M.3,Nautrup Hendrik Poulsen3,Motzoi Felix12,Briegel Hans J.3

Affiliation:

1. Forschungszentrum Jülich, Institute of Quantum Control (PGI-8), D-52425 Jülich, Germany

2. University of Cologne, Institute of Theoretical Physics, , D-50937 Köln, Germany

3. University of Innsbruck, Institute for Theoretical Physics, A-6020 Innsbruck, Austria

4. Freie Universität Berlin, Dahlem Center for Complex Quantum Systems, D-14195 Berlin, Germany.

Abstract

Shortening quantum circuits is crucial to reducing the destructive effect of environmental decoherence and enabling useful algorithms. Here, we demonstrate an improvement in such compilation tasks via a combination of using hybrid discrete-continuous optimization across a continuous gate set, and architecture-tailored implementation. The continuous parameters are discovered with a gradient-based optimization algorithm, while in tandem the optimal gate orderings are learned via a deep reinforcement learning algorithm, based on projective simulation. To test this approach, we introduce a framework to simulate collective gates in trapped-ion systems efficiently on a classical device. The algorithm proves able to significantly reduce the size of relevant quantum circuits for trapped-ion computing. Furthermore, we show that our framework can also be applied to an experimental setup whose goal is to reproduce an unknown unitary process.

Funder

German Federal Ministry of Education and Research

European Union’s Horizon Programme

Deutsche Forschungsgemeinschaft

Austrian Science Fun

European Union

Publisher

Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften

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