Abstract

Quantum parameter estimation is central to many fields such as quantum computation, communications and metrology. Optimal estimation theory has been instrumental in achieving the best accuracy in quantum parameter estimation, which is possible when we have very precise knowledge of and control over the model. However, uncertainties in key parameters underlying the system are unavoidable and may impact the quality of the estimate. We show here how quantum optical phase estimation of a squeezed state of light exhibits improvement when using a robust fixed-interval smoother designed with uncertainties explicitly introduced in parameters underlying the phase noise.

Highlights

  • Quantum parameter estimation [1] is the problem of estimating a classical variable of a quantum system

  • This work considered robust quantum phase estimation with explicitly modelled uncertainty introduced in the underlying system in a systematic state-space setting within the modern control theory paradigm

  • We constructed a robust fixed-interval smoother for continuous phase estimation of a squeezed state of light with uncertainty considered in the phase noise

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Summary

16 June 2015

Content from this work may be used under the Abstract terms of the Creative Quantum parameter estimation is central to many fields such as quantum computation, communica-. Optimal estimation theory has been instrumental in achieving the best accuracy. Any further distribution of in quantum parameter estimation, which is possible when we have very precise knowledge of and this work must maintain attribution to the control over the model. Uncertainties in key parameters underlying the system are author(s) and the title of the work, journal citation unavoidable and may impact the quality of the estimate. We show here how quantum optical phase and DOI. Estimation of a squeezed state of light exhibits improvement when using a robust fixed-interval smoother designed with uncertainties explicitly introduced in parameters underlying the phase noise

Introduction
Optimal estimator
Robust estimator
Comparison of estimators
Comparison of the errors
Resonant noise process
Uncertain model We introduce uncertainty in A as follows:
Findings
Conclusion
Full Text
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