Quick Navigation

Topics

Quantum Algorithms

Exact Identification of a Quantum Change Point

arXiv
Authors: Gael Sentís, John Calsamiglia, Ramon Munoz-Tapia

Year

2017

Paper ID

44421

Status

Preprint

Abstract Read

~2 min

Abstract Words

122

Citations

N/A

Abstract

The detection of change points is a pivotal task in statistical analysis. In the quantum realm, it is a new primitive where one aims at identifying the point where a source that supposedly prepares a sequence of particles in identical quantum states starts preparing a mutated one. We obtain the optimal procedure to identify the change point with certainty---naturally at the price of having a certain probability of getting an inconclusive answer. We obtain the analytical form of the optimal probability of successful identification for any length of the particle sequence. We show that the conditional success probabilities of identifying each possible change point show an unexpected oscillatory behaviour. We also discuss local (online) protocols and compare them with the optimal procedure.

Why This Paper Matters

  • It adds a 2017 reference point for readers tracking recent quantum research.
  • The detection of change points is a pivotal task in statistical analysis.

Paper Tools

Become a member to use research tools

Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.

Show Paper arXiv Publisher Share Cite This Paper Copy URL Compare Copy DOI Add to Reading List Category Correction Request

References & Citation Signals

Local Citation Graph (Related-Paper Links)

Current Paper #44421 #69983 Spectral Leakage and Masking Ef... #69982 Dimensionality Reduction of QAO... #69981 A Hybrid Quantum-Classical Appr... #69980 Complexity Inequalities for Qua...

External citation index: OpenAlex citation signal

Community Reactions

Quick sentiment from readers on this paper.

Score: 0
Likes: 0 Dislikes: 0

Sign in to react to this paper.

Discussion & Reviews (Moderated)

Average Rating: 0.0 / 5 (0 ratings)

No written reviews yet.