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Updated: Oct 1, 2025

Gradient Echo Quantum Memory in Warm Atomic Vapor
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Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

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Noise-assisted variational quantum thermalization.

Jonathan Foldager1, Arthur Pesah2, Lars Kai Hansen3

  • 1Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800, Kongens Lyngby, Denmark. jonf@dtu.dk.

Scientific Reports
|March 10, 2022
PubMed
Summary
This summary is machine-generated.

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This study introduces a novel quantum algorithm that uses circuit noise to efficiently prepare thermal states. The new method overcomes key challenges in quantum computing, achieving high fidelity for various systems.

Area of Science:

  • Quantum Computing
  • Quantum Information Science
  • Condensed Matter Physics

Background:

  • Preparing thermal states is crucial for quantum simulations and machine learning.
  • Existing variational methods face challenges with scalable cost functions, purification, and noise mitigation.

Purpose of the Study:

  • To propose a new algorithm for thermal state preparation that leverages quantum circuit noise.
  • To address scalability, purification, and noise challenges in near-term quantum devices.

Main Methods:

  • Developed a variational quantum circuit architecture with controlled depolarizing noise.
  • Derived a closed-form approximation of free energy as a cost function.
  • Evaluated the algorithm on diverse Hamiltonians and system sizes.

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The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry
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Related Experiment Videos

Last Updated: Oct 1, 2025

Gradient Echo Quantum Memory in Warm Atomic Vapor
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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
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Main Results:

  • Achieved high fidelity thermal state approximation for several systems.
  • Demonstrated that algorithm performance is temperature-dependent, excelling at high/low temperatures but facing challenges in intermediate ranges.
  • Identified a specific temperature range where learning the thermal state is more difficult.

Conclusions:

  • Noise-assisted thermal state preparation is a viable approach for quantum computing.
  • The proposed method offers a promising direction for overcoming limitations in variational quantum algorithms.
  • Further research into exploiting noise in quantum algorithms is warranted.