Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:1906.11028 · Foundations of Physics

Algorithmic Measurement Procedures

Aldo F. G. Solis-Labastida, Jorge G. Hirsch

Published Jul 8, 2020• 0 citations
arXivHTMLPDF
DOI
10.1007/s10701-020-00354-4

Related papers

Quantum measurements and the Abelian Stabilizer Problem

Alexei Kitaev

We present a polynomial quantum algorithm for the Abelian stabilizer problem which includes both factoring and the discrete logarithm. Thus we extend famous Shor's results. Our method is based on a procedure for measuring an eigenvalue of a unitary operator. Another application of this procedure is a polynomial quantum Fourier transform algorithm for an arbitrary finite Abelian group. The paper also contains a rather detailed introduction to the theory of quantum computation.

•Nov 20, 1995•442 citations

Measurement Implementations

Paul Busch, PEKKA J. LAHTI, Juha-Pekka Pellonpää +1 more

Theoretical and mathematical physics•Jan 1, 2016•0 citations

An approach to measurement

Stanley Gudder

Foundations of Physics•Jan 1, 1983•6 citations

Quantum measurements and the Abelian Stabilizer Problem

A. Yu. Kitaev

We present a polynomial quantum algorithm for the Abelian stabilizer problem which includes both factoring and the discrete logarithm. Thus we extend famous Shor's results. Our method is based on a procedure for measuring an eigenvalue of a unitary operator. Another application of this procedure is a polynomial quantum Fourier transform algorithm for an arbitrary finite Abelian group. The paper also contains a rather detailed introduction to the theory of quantum computation.

arXiv:quant-ph/9511026•Nov 20, 1995•472 citations

Measurement Theory

Paul Busch, PEKKA J. LAHTI

Compendium of Quantum Physics•Jan 1, 2009•3 citations

Enhanced Feedback Iterative Decoding of Sparse Quantum Codes

Yun-Jiang Wang, Barry C. Sanders, Baoming Bai +1 more

Decoding sparse quantum codes can be accomplished by syndrome-based decoding using a belief propagation (BP) algorithm. We significantly improve this decoding scheme by developing a new feedback adjustment strategy for the standard BP algorithm. In our feedback procedure, we exploit much of the information from stabilizers, not just the syndrome but also the values of the frustrated checks on individual qubits of the code and the channel model. Furthermore we show that our decoding algorithm is superior to belief propagation algorithms using only the syndrome in the feedback procedure for all cases of the depolarizing channel. Our algorithm does not increase the measurement overhead compared to the previous method, as the extra information comes for free from the requisite stabilizer measurements.

•Aug 14, 2016•35 citations
Decoding sparse quantum codes can be accomplished by syndrome-based decoding using a belief propagation (BP) algorithm. We significantly improve this decoding scheme by developing a new feedback adjustment strategy for the standard BP algorithm. In our feedback procedure, we exploit much of the information from stabilizers, not just the syndrome but also the values of the frustrated checks on individual qubits of the code and the channel model. Furthermore we show that our decoding algorithm is superior to belief propagation algorithms using only the syndrome in the feedback procedure for all cases of the depolarizing channel. Our algorithm does not increase the measurement overhead compared to the previous method, as the extra information comes for free from the requisite stabilizer measurements.