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Despite rapid progress in the field, it is still challenging to discover newways to take advantage of quantum computation: all quantum algorithms need tobe designed by hand, and quantum mechanics is notoriously counterintuitive. Inthis paper, we study how artificial intelligence, in the form of programsynthesis, may help to overcome some of these difficulties, by showing how acomputer can incrementally learn concepts relevant for quantum circuitsynthesis with experience, and reuse them in unseen tasks. In particular, wefocus on the decomposition of unitary matrices into quantum circuits, and weshow how, starting from a set of elementary gates, we can automaticallydiscover a library of new useful composite gates and use them to decompose moreand more complicated unitaries.

Discovering Quantum Circuit Components with Program Synthesis