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Exploiting Symmetry in Variational Quantum Machine Learning
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DOI：10.1103/PRXQuantum.4.010328.png)
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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, pre-cious 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 con-struct 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 equivari-ant 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 nontrivial 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.
Keyword:
MANEUVER CLASSIFICATION
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IF:
11
论文数:
919
被引数:
9.0K
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A variational eigenvalue solver on a photonic quantum processor光子量子处理器上的变分特征值求解器.png)
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