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Critical Quantum Metrology Assisted by Real-Time Feedback Control.

Raffaele Salvia1,2, Mohammad Mehboudi2, Martí Perarnau-Llobet2

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Adaptive strategies overcome limitations in quantum metrology, achieving precision beyond the shot-noise limit. This research explores Bayesian inference for parameter estimation in many-body systems near quantum critical points.

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Area of Science:

  • Quantum physics
  • Statistical mechanics
  • Information theory

Background:

  • Quantum metrology aims to precisely estimate parameters in quantum systems.
  • Quantum critical points in many-body systems offer enhanced sensitivity.
  • Bayesian inference provides a framework for parameter estimation with prior knowledge.

Purpose of the Study:

  • Investigate quantum metrology near quantum critical points using Bayesian inference.
  • Determine limitations of nonadaptive strategies in exploiting quantum critical enhancement.
  • Develop and analyze adaptive strategies to overcome these limitations.

Main Methods:

  • Derivation of a no-go result for nonadaptive strategies.
  • Analysis of adaptive strategies with real-time feedback control.
  • Application to parameter estimation in 1D spin Ising chains and Bose-Hubbard lattices.

Main Results:

  • Nonadaptive strategies fail to achieve quantum critical enhancement for large systems with limited prior knowledge.
  • Adaptive strategies can overcome the no-go result.
  • Sub-shot-noise scaling is achievable with adaptive strategies even with few measurements and substantial prior uncertainty.

Conclusions:

  • Adaptive strategies are crucial for exploiting quantum critical enhancement in metrology.
  • Real-time feedback control enables precision beyond the standard quantum limit.
  • This work provides a pathway for enhanced parameter estimation in complex quantum systems.