Southwest Quantum Information and Technology

Building spatial symmetries into parameterized quantum circuits for faster training

Presenting Author: Frederic Sauvage, Los Alamos National Laboratory
Contributing Author(s): Martin Larocca Patrick Coles Marco Cerezo

Practical success of quantum learning models hinges on having a suitable structure for the parameterized quantum circuit employed . 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.

Read this article online: https://arxiv.org/abs/2207.14413

(Session 9b : Friday from 3:45 pm - 4:15 pm)