Journal Article

FZJ-2024-03349

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning

Preti, F. (Corresponding author)FZJ* ; Schilling, M. ; Jerbi, S. ; Trenkwalder, L. M. ; Nautrup, H. P. ; Motzoi, F.FZJ* ; Briegel, H. J.

2024
Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften Wien

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Please use a persistent id in citations: doi:10.22331/q-2024-05-14-1343  doi:10.34734/FZJ-2024-03349

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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Contributing Institute(s):

1. Bioelektronik (PGI-8)
2. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5221 - Advanced Solid-State Qubits and Qubit Systems (POF4-522) (POF4-522)
2. 5222 - Exploratory Qubits (POF4-522) (POF4-522)
3. BMBF 13N16149 - QSolid - Quantencomputer im Festkörper (BMBF-13N16149) (BMBF-13N16149)
4. ML4Q - Machine Learning for Quantum (101120240) (101120240)
5. QuantAI - Artificial agency and learning in quantum environments (101055129) (101055129)

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