Matrix product state pre-training for quantum machine learning

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