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Related Concept Videos

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Related Experiment Video

Updated: Jan 4, 2026

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
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Distributed Private Randomness Distillation.

Dong Yang1,2, Karol Horodecki3,4, Andreas Winter5

  • 1Laboratory for Quantum Information, China Jiliang University, 310018 Hangzhou, China.

Physical Review Letters
|November 9, 2019
PubMed
Summary
This summary is machine-generated.

We introduce a new framework for private randomness extraction, showing local noise can surprisingly enhance randomness generation in distributed quantum systems. This resolves a long-standing problem regarding reverse coherent information.

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Area of Science:

  • Quantum Information Theory
  • Quantum Cryptography
  • Information Theory

Background:

  • The resource theory of private randomness extraction quantifies the amount of shared randomness that can be generated between parties.
  • Device-dependent and distributed scenarios present unique challenges for randomness extraction due to potential imperfections in quantum devices.

Purpose of the Study:

  • To develop a resource theory for private randomness extraction in distributed and device-dependent quantum systems.
  • To provide an operational interpretation for reverse coherent information.

Main Methods:

  • Introduction of independent random bits as bipartite states with ideal private randomness.
  • Development of a conceptual tool: virtual quantum state merging.
  • Analysis of the bipartite case to determine achievable rate regions.

Main Results:

  • Identification of free operations for the resource theory.
  • Demonstration that local noise can unexpectedly boost randomness extraction.
  • Resolution of a long-standing problem by linking reverse coherent information to private random bit generation.

Conclusions:

  • The developed resource theory offers a new perspective on distributed randomness extraction.
  • Local noise, often considered detrimental, can be beneficial in specific randomness extraction protocols.
  • Reverse coherent information is operationally interpreted as the number of private random bits generated over an eavesdropped quantum channel.