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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Retrieving Quantum Information with Active Learning.

Yongcheng Ding1,2, José D Martín-Guerrero3, Mikel Sanz2

  • 1International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Department of Physics, Shanghai University, 200444 Shanghai, China.

Physical Review Letters
|April 28, 2020
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Summary
This summary is machine-generated.

Active learning enhances quantum information retrieval by intelligently selecting data for model training. This machine learning approach achieves high accuracy with minimal data labeling, improving quantum experiment analysis.

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Area of Science:

  • Quantum Information Science
  • Machine Learning
  • Data Analysis

Background:

  • Active learning optimizes model training by selecting informative samples.
  • Quantum information retrieval is vital for designing quantum experiments.
  • Analyzing large datasets from quantum experiments presents challenges.

Purpose of the Study:

  • To apply active learning for efficient quantum information retrieval.
  • To minimize data labeling costs while maintaining high fidelity in quantum experiments.
  • To enhance the data analysis of quantum experiments using machine learning.

Main Methods:

  • Utilizing active learning strategies to identify samples with maximal uncertainty.
  • Implementing active learning for classification tasks with large quantum experimental data.
  • Evaluating the performance of active learning in rate estimation for quantum systems.

Main Results:

  • Achieved high rate estimation (nearly 90%) by labeling only 5% of samples.
  • Demonstrated minimal fidelity loss in classification tasks with active learning.
  • Showcased the efficiency of active learning in handling large quantum data outputs.

Conclusions:

  • Active learning offers an efficient method for quantum information retrieval.
  • The proposed approach significantly reduces the cost of data labeling in quantum experiments.
  • Integrating active learning into quantum experiment data analysis will advance quantum technologies.