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Efficient Bayesian Phase Estimation.

Nathan Wiebe1, Chris Granade2,3

  • 1Quantum Architectures and Computation Group, Microsoft Research, Redmond, Washington 98052, USA.

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Summary
This summary is machine-generated.

We developed rejection filtering for adaptive Bayesian phase estimation. This efficient, robust method outperforms existing algorithms and is suitable for hardware implementation.

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Area of Science:

  • Quantum information science
  • Quantum metrology
  • Quantum computing

Background:

  • Phase estimation is crucial for quantum algorithms and metrology.
  • Existing methods like Kitaev's algorithm have limitations in efficiency and robustness.
  • Adaptive Bayesian methods offer potential for improved phase estimation.

Purpose of the Study:

  • Introduce a novel rejection filtering method for adaptive Bayesian phase estimation.
  • Demonstrate the advantages of this new approach over existing techniques.
  • Highlight its suitability for practical implementation in quantum systems.

Main Methods:

  • Developed a rejection filtering technique for adaptive Bayesian phase estimation.
  • Implemented the method to track time-dependent eigenstates.
  • Tested its performance against depolarizing noise and failure recovery.

Main Results:

  • The rejection filtering method is classically efficient and easy to implement.
  • Achieves Heisenberg limited scaling, the theoretical best for precision.
  • Demonstrates robustness against depolarizing noise and ability to recover from failures.
  • Outperforms established algorithms like Kitaev's method.

Conclusions:

  • Rejection filtering provides a superior approach to adaptive Bayesian phase estimation.
  • The method's efficiency, robustness, and practical features make it valuable for quantum technologies.
  • Suitable for implementation on field-programmable gate arrays (FPGAs).