Building spatial symmetries into parameterized quantum circuits for faster training

Abstract

Practical success of quantum learning models hinges on having a suitable structure for the parameterized quantum circuit. Such structure is defined both by the types of gates employed and by the correlations of their parameters. While much research has been devoted to devising adequate gate-sets, typically respecting some symmetries of the problem, very little is known about how their parameters should be structured. In this work, we show that an ideal parameter structure naturally emerges when carefully considering spatial symmetries (i.e., the symmetries that are permutations of parts of the system under study). Namely, we consider the automorphism group of the problem Hamiltonian, leading us to develop a circuit construction that is equivariant under this symmetry group. The benefits of our novel circuit structure, called ORB, are numerically probed in several ground-state problems. We find a consistent improvement (in terms of circuit depth, number of parameters required, and gradient magnitudes) compared to literature circuit constructions.

14 pages + 8 figures

References in corpus (7)

• A Quantum Approximate Optimization Algorithm
• An introduction to quantum machine learning
• Hybrid quantum-classical algorithms and quantum error mitigation
• Exploiting symmetry in variational quantum machine learning
• Group-Invariant Quantum Machine Learning
• Quantum Graph Neural Networks
• Variational Hamiltonian Diagonalization for Dynamical Quantum Simulation

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