Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
GPT-5.1
GPT-5.1 130 tok/s
Gemini 3.0 Pro 29 tok/s Pro
Gemini 2.5 Flash 145 tok/s Pro
Kimi K2 191 tok/s Pro
Claude Sonnet 4.5 34 tok/s Pro
2000 character limit reached

Quantum Period-Finding using One-Qubit Reduced Density Matrices (2511.09896v1)

Published 13 Nov 2025 in quant-ph and physics.comp-ph

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 $O(2n)$ 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.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Authors (1)

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 1 tweet and received 0 likes.

Upgrade to Pro to view all of the tweets about this paper: