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

• URL: http://arxiv.org/abs/2307.05744v2
• Date: Fri, 3 May 2024 15:33:07 GMT
• Title: Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning
• Authors: Francesco Preti, Michael Schilling, Sofiene Jerbi, Lea M. Trenkwalder, Hendrik Poulsen Nautrup, Felix Motzoi, Hans J. Briegel,
• Abstract summary: We show that we can significantly reduce the size of relevant quantum circuits for trapped-ion computing. Our framework can also be applied to an experimental setup whose goal is to reproduce an unknown unitary process.
• Score: 1.7087507417780985
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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