Discovering quantum circuit components with program synthesis

Sarra L, Ellis K, Marquardt F (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Machine Learning: Science and Technology Institute of Physics Publishing (IOP)

Book Volume: 5

Article Number: 025029

Journal Issue: 2

DOI: 10.1088/2632-2153/ad4252

Abstract

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.

Involved external institutions

Max-Planck-Institut für die Physik des Lichts (MPL) / Max Planck Institute for the Science of Light DE Germany (DE) Cornell University US United States (USA) (US)

How to cite

APA:

Sarra, L., Ellis, K., & Marquardt, F. (2024). Discovering quantum circuit components with program synthesis. Machine Learning: Science and Technology, 5(2). https://doi.org/10.1088/2632-2153/ad4252

MLA:

Sarra, Leopoldo, Kevin Ellis, and Florian Marquardt. "Discovering quantum circuit components with program synthesis." Machine Learning: Science and Technology 5.2 (2024).

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