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.
- Publication:
-
Quantum Science and Technology
- Pub Date:
- July 2022
- DOI:
- 10.1088/2058-9565/ac7073
- arXiv:
- arXiv:2106.05742
- Bibcode:
- 2022QS&T....7c5014D
- Keywords:
-
- quantum;
- machine learning;
- NISQ;
- matrix produce state;
- VQE;
- Quantum Physics
- E-Print:
- v2: Added short comparison to entanglement devised barren plateau mitigation - relevant paper missed in first submission