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

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.

25 pages, 9 figures

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• Realizing a Continuous Set of Two-Qubit Gates Parameterized by an Idle Time
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• Exponentiation of Parametric Hamiltonians via Unitary interpolation
• Statistical evaluation and optimization of entanglement purification protocols
• Molecular Quantum Control Algorithm Design by Reinforcement Learning