Hidden Symmetry Subgroup Problems

TL;DR

This paper introduces the Hidden Symmetry Subgroup Problem (HSSP), a generalization of the Hidden Subgroup Problem, and develops efficient quantum algorithms for specific cases, notably for multivariate quadratic polynomials.

Contribution

The paper presents a new framework unifying various algebraic problems and provides the first efficient quantum algorithms for hidden polynomial problems.

Findings

Efficient quantum algorithms for multivariate quadratic polynomial problems.

Reduction of HSSP to HSP in certain cases.

Improved algorithms for polynomial function graph problems.

Abstract

We advocate a new approach of addressing hidden structure problems and finding efficient quantum algorithms. We introduce and investigate the Hidden Symmetry Subgroup Problem (HSSP), which is a generalization of the well-studied Hidden Subgroup Problem (HSP). Given a group acting on a set and an oracle whose level sets define a partition of the set, the task is to recover the subgroup of symmetries of this partition inside the group. The HSSP provides a unifying framework that, besides the HSP, encompasses a wide range of algebraic oracle problems, including quadratic hidden polynomial problems. While the HSSP can have provably exponential quantum query complexity, we obtain efficient quantum algorithms for various interesting cases. To achieve this, we present a general method for reducing the HSSP to the HSP, which works efficiently in several cases related to symmetries of polynomials. The HSSP therefore connects in a rather surprising way certain hidden polynomial problems with the HSP. Using this connection, we obtain the first efficient quantum algorithm for the hidden polynomial problem for multivariate quadratic polynomials over fields of constant characteristic. We also apply the new methods to polynomial function graph problems and present an efficient quantum procedure for constant degree multivariate polynomials over any field. This result improves in several ways the currently known algorithms.