Discovering Quantum Circuit Components with Program Synthesis

Sarra, Leopoldo; Ellis, Kevin; Marquardt, Florian

doi:10.1088/2632-2153/ad4252

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Discovering Quantum Circuit Components with Program Synthesis

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Sarra, Leopoldo
Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society;
Department of Physics, Friedrich-Alexander Universität Erlangen-Nürnberg;

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Marquardt, Florian
Marquardt Division, Max Planck Institute for the Science of Light, Max Planck Society;
Department of Physics, Friedrich-Alexander Universität Erlangen-Nürnberg;

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https:iopscience.iop.org:article:10.1088:2632-2153:ad4252:pdf.pdf
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引用

Sarra, L., Ellis, K., & Marquardt, F. (2024). Discovering Quantum Circuit Components with Program Synthesis. Machine Learning: Science and Technology, 5:. doi:10.1088/2632-2153/ad4252.

引用: https://hdl.handle.net/21.11116/0000-000D-11D4-0

要旨

Despite rapid progress in the field, it is still challenging to discover new ways to leverage quantum
computation: all quantum algorithms must be designed by hand, and quantum mechanics is
notoriously counterintuitive. In this paper, we study how artificial intelligence, in the form of
program synthesis, may help overcome some of these difficulties, by showing how a computer can
incrementally learn concepts relevant to quantum circuit synthesis with experience, and reuse
them in unseen tasks. In particular, we focus on the decomposition of unitary matrices into
quantum circuits, and show how, starting from a set of elementary gates, we can automatically
discover a library of useful new composite gates and use them to decompose increasingly
complicated unitaries.