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Direct Fidelity Estimation of Quantum States Using Machine Learning.

Xiaoqian Zhang1, Maolin Luo1, Zhaodi Wen2

  • 1School of Physics and State Key Laboratory of Optoelectronic Materials and Technologies, Sun Yat-sen University, Guangzhou 510000, China.

Physical Review Letters
|October 8, 2021
PubMed
Summary
This summary is machine-generated.

We developed a new machine learning method to verify quantum state fidelity. This approach requires few measurements, regardless of system size, for accurate quantum information applications.

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Area of Science:

  • Quantum Information Science
  • Machine Learning Applications

Background:

  • Verifying quantum state fidelity is crucial for quantum applications.
  • Existing fidelity estimation methods can be resource-intensive.

Purpose of the Study:

  • To propose a novel machine learning-based approach for estimating quantum state fidelity.
  • To offer a method that is efficient and scalable for various quantum systems.

Main Methods:

  • Utilizing machine learning techniques to predict quantum state fidelity.
  • Developing a method that requires a minimal and fixed number of measurement settings.

Main Results:

  • The proposed method is applicable to arbitrary quantum states.
  • The number of measurement settings is independent of the system size.
  • Achieved ±1% precision for a five-qubit state with only four settings.

Conclusions:

  • This machine learning approach offers a practical solution for quantum state fidelity verification.
  • The method has broad potential for widespread adoption in quantum information processing.