Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning
1Forschungszentrum Jülich, Institute of Quantum Control (PGI-8), D-52425 Jülich, Germany
2University of Cologne, Institute of Theoretical Physics, , D-50937 Köln, Germany
3University of Innsbruck, Institute for Theoretical Physics, A-6020 Innsbruck, Austria
4Freie Universität Berlin, Dahlem Center for Complex Quantum Systems, D-14195 Berlin, Germany.
Published: | 2024-05-14, volume 8, page 1343 |
Eprint: | arXiv:2307.05744v2 |
Doi: | https://doi.org/10.22331/q-2024-05-14-1343 |
Citation: | Quantum 8, 1343 (2024). |
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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.
![](https://quantum-journal.org/wp-content/uploads/2024/05/bilevel_optcirc1.png)
Featured image: A simple representation of the bi-level optimization scheme. The agent constructs a variational circuit by choosing which gates to put on the circuit. The circuit is then optimized with respect to the variational parameters by a gradient-based optimizer. Based on the results of the continuous optimization, an appropriate reward function is constructed to guide the agent towards the correct policy. The robot picture was generated by the authors using Midjourney.
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