Exploiting Symmetry in Variational Quantum Machine Learning
| Autor: | Johannes Jakob Meyer, Marian Mularski, Elies Gil-Fuster, Antonio Anna Mele, Francesco Arzani, Alissa Wilms, Jens Eisert |
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| Rok vydání: | 2023 |
| Předmět: |
FOS: Computer and information sciences
Quantum Physics Computer Science - Machine Learning Artificial neural networks General Computer Science Computer Science - Artificial Intelligence Applied Mathematics 500 Naturwissenschaften und Mathematik::530 Physik::530 Physik FOS: Physical sciences General Physics and Astronomy Deep learning Machine Learning (cs.LG) Electronic Optical and Magnetic Materials Artificial Intelligence (cs.AI) Machine learning Quantum computation Quantum gates Electrical and Electronic Engineering Quantum Physics (quant-ph) Mathematical Physics Quantum information theory |
| Zdroj: | PRX Quantum. 4 |
| ISSN: | 2691-3399 |
| DOI: | 10.1103/prxquantum.4.010328 |
| Popis: | Variational quantum machine learning is an extensively studied application of near-term quantum computers. The success of variational quantum learning models crucially depends on finding a suitable parametrization of the model that encodes an inductive bias relevant to the learning task. However, precious little is known about guiding principles for the construction of suitable parametrizations. In this work, we holistically explore when and how symmetries of the learning problem can be exploited to construct quantum learning models with outcomes invariant under the symmetry of the learning task. Building on tools from representation theory, we show how a standard gateset can be transformed into an equivariant gateset that respects the symmetries of the problem at hand through a process of gate symmetrization. We benchmark the proposed methods on two toy problems that feature a non-trivial symmetry and observe a substantial increase in generalization performance. As our tools can also be applied in a straightforward way to other variational problems with symmetric structure, we show how equivariant gatesets can be used in variational quantum eigensolvers. Comment: 25 pages, 15 figures, comments welcome |
| Databáze: | OpenAIRE |
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