Abstract Quantum inverse problem (QIP) is the problem of estimating an unknown quantum system from a set of measurements, whereas the classical counterpart is the inverse problem of estimating a distribution from a set of observations. In this paper, we present a neural-network-based method for QIPs, which has been widely explored for its classical counterpart. The proposed method utilizes the quantumness of the QIPs and takes advantage of the computational power of neural networks to achieve remarkable efficiency for the quantum state estimation. We test the method on the problem of maximum entropy estimation of an unknown state ρ from partial information both numerically and experimentally. Our method yields high fidelity, efficiency and robustness for both numerical experiments and quantum optical experiments.Abstract Quantum inverse problem (QIP) is the problem of estimating an unknown quantum system from a set of measurements, whereas the classical counterpart is the inverse problem of estimating a distribution from a set of observations. In this paper, we present a neural-network-based method for QIPs, which has been widely explored for its classical counterpart. The proposed method utilizes the quantumness of the QIPs and takes advantage of the computational power of neural networks to achieve remarkable efficiency for the quantum state estimation. We test the method on the problem of maximum entropy estimation of an unknown state ρ from partial information both numerically and experimentally. Our method yields high fidelity, efficiency and robustness for both numerical experiments and quantum optical experiments.

arXiv:2005.01540New Journal of PhysicsMay 17, 202210 citations

Inverse problem of photocount statistics: Applicability criterion for the inverse Bernoulli transform method

P. P. Gostev, Sergey A Magnitskiy, A. S. Chirkin

It is shown that the applicability conditions for the inverse Bernoulli transform method to solve the inverse problem of photocount statistics are determined by the fulfillment of the associativity condition for multiplying the matrices included in this transformation. A general criterion for evaluating the photocount distributions ${Q}_{m}$ in the case of few-photon light, which makes it possible to establish whether the solution to the inverse problem of photocount statistics by the inverse Bernoulli transform method is applicable for $\ensuremath{\eta}<0.5$, is found. As an example of the application of the obtained criterion, the critical quantum efficiency ${\ensuremath{\eta}}_{\text{cr}}$ is found for compound Poisson distribution, below which the solution of the inverse problem of photocount statistics becomes incorrect. Additionally it is shown that the normalization of ${Q}_{m}$ is not sufficient to obtain a correct solution using the inverse Bernoulli transform.It is shown that the applicability conditions for the inverse Bernoulli transform method to solve the inverse problem of photocount statistics are determined by the fulfillment of the associativity condition for multiplying the matrices included in this transformation. A general criterion for evaluating the photocount distributions ${Q}_{m}$ in the case of few-photon light, which makes it possible to establish whether the solution to the inverse problem of photocount statistics by the inverse Bernoulli transform method is applicable for $\ensuremath{\eta}<0.5$, is found. As an example of the application of the obtained criterion, the critical quantum efficiency ${\ensuremath{\eta}}_{\text{cr}}$ is found for compound Poisson distribution, below which the solution of the inverse problem of photocount statistics becomes incorrect. Additionally it is shown that the normalization of ${Q}_{m}$ is not sufficient to obtain a correct solution using the inverse Bernoulli transform.

Physical Review AApr 24, 20234 citations

{Q}_{m}

in the case of few-photon light, which makes it possible to establish whether the solution to the inverse problem of photocount statistics by the inverse Bernoulli transform method is applicable for $\ensuremath{\eta}<0.5$, is found. As an example of the application of the obtained criterion, the critical quantum efficiency ${\ensuremath{\eta}}_{\text{cr}}$ is found for compound Poisson distribution, below which the solution of the inverse problem of photocount statistics becomes incorrect. Additionally it is shown that the normalization of

{Q}_{m}

is not sufficient to obtain a correct solution using the inverse Bernoulli transform.