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Resource-efficient quantum correlation measurements via multicopy neural network methods.

Patrycja Tulewicz1,2, Karol Bartkiewicz3,4, Adam Miranowicz3,4

  • 1Institute of Spintronics and Quantum Information, Faculty of Physics and Astronomy, Adam Mickiewicz University, Poznań, 61-614, Poland. pattul@amu.edu.pl.

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We developed a new method using artificial neural networks (ANNs) and multicopy measurements to efficiently characterize quantum entanglement and nonlocality. This AI-assisted approach significantly reduces measurement needs compared to traditional quantum state tomography (QST).

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Area of Science:

  • Quantum Information Science
  • Quantum Computing
  • Artificial Intelligence

Background:

  • Characterizing complex quantum properties like entanglement and nonlocality is crucial but challenging.
  • Traditional methods such as quantum state tomography (QST) require exponentially scaling resources with system size.
  • Efficient and robust measurement strategies are needed for current noisy intermediate-scale quantum (NISQ) hardware.

Purpose of the Study:

  • To propose and experimentally validate a resource-efficient method for measuring quantum correlations.
  • To reduce the information requirements for quantifying entanglement and Bell nonlocality.
  • To enhance the noise robustness of quantum measurements on NISQ devices.

Main Methods:

  • Combining multicopy measurements with artificial neural networks (ANNs) for quantum state characterization.
  • Utilizing Shapley (SHAP) analysis to optimize measurement projection sets.
  • Implementing and testing the approach on IBMQ quantum processors with transmon qubits.
  • Comparing results with standard QST and applying noise mitigation techniques.

Main Results:

  • Achieved a 67% reduction in measurement requirements compared to QST.
  • Successfully measured quantum correlations for Werner and Horodecki states under noise channels.
  • Trained ANNs to accurately estimate entanglement and nonlocality measures from reduced measurement sets.
  • Demonstrated improved noise robustness of the AI-assisted multicopy measurement strategy.

Conclusions:

  • The proposed AI-assisted multicopy measurement approach offers a practical and resource-efficient alternative to QST for quantum correlation characterization.
  • This method simplifies complex quantum measurements and enhances error robustness on NISQ hardware.
  • It represents a significant advancement for practical quantum information processing and AI-assisted quantum metrology.