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End-to-end variational quantum sensing.

Benjamin MacLellan1,2,3,4, Piotr Roztocki4, Stefanie Czischek5

  • 1University of Waterloo, Department of Physics & Astronomy, 200 University Ave., Waterloo, ON Canada.

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We developed a new framework for quantum sensing that uses adaptive models to improve precision beyond classical limits. This approach helps overcome noise and design challenges for practical quantum sensor applications.

Keywords:
Quantum informationQuantum mechanicsQuantum metrology

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Area of Science:

  • Quantum physics
  • Quantum information science
  • Sensor technology

Background:

  • Quantum correlations offer sensing capabilities beyond classical limits.
  • Practical quantum sensor realization is hindered by noise and architectural constraints.
  • Optimized theoretical and numerical frameworks are essential for translating quantum advantage into practice.

Purpose of the Study:

  • To present an end-to-end variational framework for optimizing and analyzing quantum sensing protocols.
  • To develop trainable, adaptive models for quantum sensor dynamics and estimation.
  • To demonstrate the framework's generality across different qubit architectures.

Main Methods:

  • Utilizing parameterized quantum circuits as trainable models for quantum sensor dynamics.
  • Employing neural networks as adaptive models for estimation processes.
  • Adapting the framework to experimentally relevant ansätze for trapped-ion and photonic systems.

Main Results:

  • The framework provides an end-to-end approach for quantum sensing protocol design and analysis.
  • It enables direct quantification of noise and finite data sampling impacts.
  • Demonstrated adaptability to trapped-ion and photonic qubit architectures.

Conclusions:

  • End-to-end variational approaches are crucial for practical quantum sensing.
  • The presented framework facilitates the design of powerful quantum sensing tools.
  • This work supports the widespread adoption of quantum advantage in sensing applications.