Dborin, James;
Barratt, Fergus;
Wimalaweera, Vinul;
Wright, Lewis;
Green, Andrew G;
(2022)
Matrix product state pre-training for quantum machine learning.
Quantum Science and Technology
, 7
(3)
, Article 035014. 10.1088/2058-9565/ac7073.
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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.
| Type: | Article |
|---|---|
| Title: | Matrix product state pre-training for quantum machine learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/2058-9565/ac7073 |
| Publisher version: | https://doi.org/10.1088/2058-9565/ac7073 |
| Language: | English |
| Additional information: | Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
| Keywords: | Science & Technology, Physical Sciences, Quantum Science & Technology, Physics, Multidisciplinary, Physics, quantum, machine learning, NLSQ, matrix produce state, VQE |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > London Centre for Nanotechnology UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10158073 |
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