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.
@article{arxiv.2106.05742,
title = {Matrix Product State Pre-Training for Quantum Machine Learning},
author = {James Dborin and Fergus Barratt and Vinul Wimalaweera and Lewis Wright and Andrew G. Green},
journal= {arXiv preprint arXiv:2106.05742},
year = {2021}
}
Comments
v2: Added short comparison to entanglement devised barren plateau mitigation - relevant paper missed in first submission