Matrix Product State Pre-Training for Quantum Machine Learning

TL;DR

This paper introduces a matrix product state pre-training method that enhances the training efficiency of parametrised quantum circuits in quantum machine learning tasks, addressing gradient vanishing issues.

Contribution

It presents a novel circuit pre-training approach using tensor network methods, specifically matrix product states, to improve quantum circuit training.

Findings

Accelerates training of PQCs in supervised learning

Improves energy minimization efficiency

Enhances combinatorial optimization performance

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. Training PQCs relies on methods to overcome the fact that the gradients of PQCs vanish exponentially in the size of the circuits used. 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.