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Discovering Quantum Circuit Components with Program Synthesis
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Despite rapid progress in the field, it is still challenging to discover new ways to take advantage of quantum computation: all quantum algorithms need to 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 to overcome some of these difficulties, by showing how a computer can incrementally learn concepts relevant for 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 we show how, starting from a set of elementary gates, we can automatically discover a library of new useful composite gates and use them to decompose more and more complicated unitaries.
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Leveraging Diffusion Models for Parameterized Quantum Circuit Generation
A diffusion model is extended to generate both the architecture and the continuous gate parameters of parameterized quantum circuits, conditioned on target performance like fidelity or accuracy.
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