Practical resources and measurements for lossy optical quantum metrology

Oh, Changhun; Lee, Su Yong; Nha, Hyunchul; Jeong, Hyunseok

doi:10.1103/physreva.96.062304

Cited by 27 publications

(37 citation statements)

References 47 publications

(63 reference statements)

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“…[43]. Note that preparing the squeezed thermal state input without a photon-loss channel is equivalent to using the optimal entangled Gaussian state with a photon-loss channel after adjusting appropriate parameters when the photon-loss rates are equal to each other [47]. Let us find the implementation of the Gaussian measurement corresponding to M .…”

Section: Appendix B: Maximum Variance Ofĝ *mentioning

confidence: 99%

Optimal distributed quantum sensing using Gaussian states

Lee

Lie

et al. 2020

Phys. Rev. Research

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We find and investigate the optimal scheme of distributed quantum sensing using Gaussian states for estimation of the average of independent phase shifts. We show that the ultimate sensitivity is achievable by using an entangled symmetric Gaussian state, which can be generated using a single-mode squeezed vacuum state, a beam-splitter network, and homodyne detection on each output mode in the absence of photon loss. Interestingly, the maximal entanglement of a symmetric Gaussian state is not optimal although the presence of entanglement is advantageous as compared to the case using a product symmetric Gaussian state. It is also demonstrated that when loss occurs, homodyne detection and other types of Gaussian measurements compete for better sensitivity, depending on the amount of loss and properties of a probe state. None of them provide the ultimate sensitivity, indicating that non-Gaussian measurements are required for optimality in lossy cases. Our general results obtained through a full-analytical investigation will offer important perspectives to the future theoretical and experimental study for distributed Gaussian quantum sensing.

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Section: Appendix B: Maximum Variance Ofĝ *mentioning

confidence: 99%

Optimal distributed quantum sensing using Gaussian states

Lee

Lie

et al. 2020

Phys. Rev. Research

Self Cite

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“…Therefore, to prevent the exaggerated estimation of QFI, an external parameter reference is invoked to allow a well-defined parameter ϕ. In the absence of parameter reference, one has to pay attention to what parameter encoding strategies are required to the corresponding experimental setup for attaining the accurate QFI [41][42][43].…”

Section: A Quantum Fisher Informationmentioning

confidence: 99%

Phase-sensitive nonclassical properties in quantum metrology with a displaced squeezed vacuum state

et al. 2021

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We predict that the phase-dependent error distribution of locally unentangled quantum states directly affects quantum parameter estimation accuracy. Therefore, we employ the displaced squeezed vacuum (DSV) state as a probe state and investigate an interesting question of the phase-sensitive nonclassical properties in DSV's metrology. We found that the accuracy limit of parameter estimation is a function of the phase-sensitive parameter φ − θ/2 with a period π. We show that when φ − θ/2 ∈ [kπ/2, 3kπ/4) (k ∈ Z), we can obtain the accuracy of parameter estimation approaching the ultimate quantum limit through using the DSV state with the larger displacement and squeezing strength, whereas φ − θ/2 ∈ (3kπ/4, kπ] (k ∈ Z), the optimal estimation accuracy can be acquired only when the DSV state degenerates to squeezed-vacuum state.

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“…In particular, photon number detection can avoid the dependence of phase estimation accuracy on the true phase value because it fully utilizes the statistical feature of each photon. However, these measurement methods for Heisenberg scaling are not implemented readily in practical quantum physical systems and more significantly, they are more sensitive to photon loss in lossy MZI 46,47 .…”

Section: Machine Learning For Quantum Phase Estimationmentioning

confidence: 99%

“…In our algorithm, we mainly consider the linear photon loss caused by photon loss inside the interferometer and photon loss due to the inefficient detector. Specifically, assuming the losses are existed in two arms and inserted between the first BS and phase shifter 47 . Here, the photon losses are symmetric in two arms, i.e., , and we aim to validate whether our algorithm is robust against linear photon losses.…”

Section: Machine Learning For Quantum Phase Estimationmentioning

confidence: 99%

Continuous-variable Quantum Phase Estimation based on Machine Learning

Xiao

Huang

Fan

et al. 2019

Sci Rep

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Making use of the general physical model of the Mach-Zehnder interferometer with photon loss which is a fundamental physical issue, we investigate the continuous-variable quantum phase estimation based on machine learning approach, and an efficient recursive Bayesian estimation algorithm for Gaussian states phase estimation has been proposed. With the proposed algorithm, the performance of the phase estimation may be improved distinguishably. For example, the physical limits (i.e., the standard quantum limit and Heisenberg limit) for the phase estimation precision may be reached in more efficient ways especially in the situation of the prior information being employed, the range for the estimated phase parameter can be extended from [0, π /2] to [0, 2 π ] compared with the conventional approach, and influences of the photon losses on the output parameter estimation precision may be suppressed dramatically in terms of saturating the lossy bound. In addition, the proposed algorithm can be extended to the time-variable or multi-parameter estimation framework.

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Practical resources and measurements for lossy optical quantum metrology

Cited by 27 publications

References 47 publications

Optimal distributed quantum sensing using Gaussian states

Optimal distributed quantum sensing using Gaussian states

Phase-sensitive nonclassical properties in quantum metrology with a displaced squeezed vacuum state

Continuous-variable Quantum Phase Estimation based on Machine Learning

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