Molecular representations of quantum circuits for quantum machine learning

Abstract We establish an isomorphism between quantum circuits (QCs) and a subspace of polyatomic molecules, which suggests that molecules can be used as descriptors of QCs for quantum machine learning. Our numerical results show that the performance of QCs for quantum support vector machines can be characterized by dimensionality-reduced molecular fingerprints as well as by the size of the largest and smallest Gershgorin circles derived from the Coulomb matrices of the corresponding molecules. This can be used to restrict the search space for the compositional optimization of QCs. We show that a high accuracy of a quantum algorithm can be achieved with high probability by sampling from a specific set of molecules. This work implies that quantum ansatz optimization can benefit from advances in cheminformatics and suggests an approach to identify key elements that enhance the accuracy of a quantum algorithm by mapping QCs onto molecules and exploring correlations between physical properties of molecules and circuit performance.