Learning and Testing Algorithms for the Clifford Group

TL;DR

This paper introduces optimal algorithms for identifying and testing Clifford group unitaries and their hierarchy levels, with applications to quantum information processing.

Contribution

It provides the first exact, query-efficient algorithm for Clifford identification and extends it to the Gottesman-Chuang hierarchy, including testing methods for proximity.

Findings

Optimal O(n) query algorithm for Clifford identification

Extension of algorithms to the Gottesman-Chuang hierarchy

Clifford testing algorithm to distinguish close and far unitaries

Abstract

Given oracle access to an unknown unitary C from the Clifford group and its conjugate, we give an exact algorithm for identifying C with O(n) queries, which we prove is optimal. We then extend this to all levels of the Gottesman-Chuang hierarchy (also known as the C_k hierarchy). Further, for unitaries not in the hierarchy itself but known to be close to an element of the hierarchy, we give a method of finding this close element. We also present a Clifford testing algorithm that decides whether a given black-box unitary is close to a Clifford or far from every Clifford.