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Area of Science:

  • Quantum Information Science
  • Condensed Matter Physics
  • Quantum Computing

Background:

  • Entanglement is fundamental to quantum technologies and understanding quantum correlations in many-body systems.
  • Measuring entanglement in generic mixed states typically requires reconstructive quantum tomography, which scales exponentially with system size.

Purpose of the Study:

  • To propose a novel, efficient method for measuring entanglement between arbitrary subsystems.
  • To overcome the limitations of traditional quantum tomography for large and complex quantum systems.

Main Methods:

  • A machine-learning-assisted scheme utilizing a neural network.
  • Learning the nonlinear function connecting measurable moments to logarithmic negativity.
  • Employing a number of measurements scaling linearly with subsystem size (O(N_{A}+N_{B})).

Main Results:

  • The proposed method enables entanglement measurement without prior knowledge of the quantum state.
  • It significantly reduces the number of required measurements compared to quantum tomography.
  • The approach is applicable to arbitrary subsystems within a larger system.

Conclusions:

  • This work introduces a practical and scalable method for quantifying entanglement.
  • It facilitates entanglement characterization in various quantum systems, including strongly correlated many-body systems.
  • The findings pave the way for advanced studies in quantum information and condensed matter physics.