Matrix product state pre-training for quantum machine learning

Abstract

Hybrid quantum–classical algorithms are a promising candidate for developing uses for NISQ devices. In particular, parametrised quantum circuits (PQCs) paired with classical optimizers have been used as a basis for quantum chemistry and quantum optimization problems. Tensor network methods are being increasingly used as a classical machine learning tool, as well as a tool for studying quantum systems. We introduce a circuit pre-training method based on matrix product state machine learning methods, and demonstrate that it accelerates training of PQCs for both supervised learning, energy minimization, and combinatorial optimization.