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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Machine learning on quantum experimental data toward solving quantum many-body problems.

Gyungmin Cho1, Dohun Kim2

  • 1Department of Physics and Astronomy, and Institute of Applied Physics, Seoul National University, Seoul, 08826, South Korea. km950501@snu.ac.kr.

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Summary
This summary is machine-generated.

We integrated classical machine learning (ML) with quantum computing data to solve complex problems in many-body physics. This hybrid approach successfully processed quantum experimental data for systems up to 44 qubits.

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Many-Body Physics

Background:

  • Quantum hardware generates data intractable for classical emulation.
  • Classical machine learning (ML) integration with quantum data offers pattern discovery potential.
  • Current noisy quantum computers limit hybrid approach applications.

Purpose of the Study:

  • Extend hybrid quantum-classical ML to many-body physics problems.
  • Predict ground state properties and classify quantum phases.
  • Demonstrate ML algorithm effectiveness on refined quantum experimental data.

Main Methods:

  • Utilized superconducting quantum hardware with 127 qubits.
  • Applied error-reducing procedures to acquire refined quantum data.
  • Implemented classical ML algorithms for quantum system analysis.

Main Results:

  • Successfully applied classical ML algorithms to quantum experimental data.
  • Demonstrated feasibility for systems up to 44 qubits.
  • Verified scalability and effectiveness of ML for quantum data processing.

Conclusions:

  • The hybrid quantum-classical ML approach is effective for many-body physics.
  • Refined quantum data enables advanced ML applications.
  • This method shows promise for analyzing complex quantum systems.