Enforcing exact permutation and rotational symmetries in the application of quantum neural network on point cloud datasets

TL;DR

This paper introduces a quantum neural network architecture that enforces exact permutation and rotational symmetries, improving the processing of symmetric point cloud data for tasks like image classification and particle physics analysis.

Contribution

It presents a novel QNN structure that is exactly invariant to both rotations and permutations, advancing symmetry incorporation in quantum machine learning.

Findings

Successfully demonstrated on 2D image classification tasks

Effective in identifying high-energy particle decays with Lorentz symmetry

Achieves exact invariance to symmetries in quantum neural networks

Abstract

Recent developments in the field of quantum machine learning have promoted the idea of incorporating physical symmetries in the structure of quantum circuits. A crucial milestone in this area is the realization of $S_{n}$-permutation equivariant quantum neural networks (QNN) that are equivariant under permutations of input objects. In this work, we focus on encoding the rotational symmetry of point cloud datasets into the QNN. The key insight of the approach is that all rotationally invariant functions with vector inputs are equivalent to a function with inputs of vector inner products. We provide a novel structure of QNN that is exactly invariant to both rotations and permutations, with its efficacy demonstrated numerically in the problems of two-dimensional image classifications and identifying high-energy particle decays, produced by proton-proton collisions, with the $SO(1,3)$ Lorentz symmetry.