Quantum Period-Finding using One-Qubit Reduced Density Matrices

TL;DR

This paper introduces a novel approach to quantum period-finding that uses single-qubit reduced density matrices instead of full quantum state measurements, potentially simplifying the process and reducing resource requirements.

Contribution

The paper demonstrates that period information can be extracted from one-qubit reduced density matrices, offering a new perspective on quantum algorithms and potential for more efficient simulations.

Findings

1. 1-RDMs contain sufficient information to determine the period.

2. Period can be reconstructed from $O(n)$ one-qubit marginals.

3. Approach offers a potential compression of quantum information.

Abstract

The quantum period-finding (QPF) algorithm can compute the period of a function exponentially faster than the best-known classical algorithm. In standard QPF, the output state has a primary contribution from $r$ high-probability bit strings, where $r$ is the period. Measurement of this state, combined with continued fraction analysis, reveals the unknown period. Here, we explore a different approach to QPF, where the period is obtained from single-qubit quantities $-$ specifically, the set of one-qubit reduced density matrices (1-RDMs) $-$ rather than the output bit strings of the entire quantum circuit. Using state-vector simulations, we compute the 1-RDMs of the QPF circuit for a generic periodic function. Analysis of these 1-RDMs as a function of period reveals distinctive patterns, which allows us to obtain the unknown period from the 1-RDMs using a numerical root-finding approach. Our results show that the 1-RDMs $-$ a set of $O(n)$ one-qubit marginals $-$ contain enough information to reconstruct the period, which is typically obtained by sampling the space of $O(2^n)$ bit strings. Conceptually, this can be viewed as a "compression" of the information in the QPF algorithm, which enables period-finding from $n$ one-qubit marginals. Our results motivate the development of approximate simulations of reduced density matrices to design novel period-finding algorithms.