Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation

Fiderer, Lukas J.; Schuff, Jonas; Braun, Daniel

doi:10.1103/prxquantum.2.020303

Cited by 49 publications

(37 citation statements)

References 41 publications

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“…Adaptive Bayesian estimation has also been experimentally applied to multiparameter estimation by Valeri et al [193]. Recently, Fiderer et al [194] also used the neural network for the adaptive Bayesian quantum estimation. Joint measurements on conjugate observables have also been demonstrated [195].…”

Section: Optimization Of the Measurementmentioning

confidence: 99%

Optimal Scheme for Quantum Metrology

Liu,

Zhang,

Chen

et al. 2021

Preprint

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Quantum metrology can achieve far better precision than classical metrology, and is one of the most important applications of quantum technologies in the real world. To attain the highest precision promised by quantum metrology, all steps of the schemes need to be optimized, which include the state preparation, parametrization and measurement. Here we review the recent progresses on the optimization of these steps, which are essential for the identification and achievement of the ultimate precision limit in quantum metrology. We hope this provides a useful reference for the researchers in quantum metrology and related fields.

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Section: Optimization Of the Measurementmentioning

confidence: 99%

Optimal Scheme for Quantum Metrology

Liu,

Zhang,

Chen

et al. 2021

Preprint

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“…Refs. 46,47 and references within). In the case of and meshes, instead, calibration can be carried out by directing light to each MZ in a specific order.…”

Section: Bias In the Calibration Processmentioning

confidence: 99%

Emergence of biased errors in imperfect photonic circuits

Flamini¹

2021

Preprint

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“…These are capable of handling large data sets and of solving tasks for which they have not been explicitly programmed; applications range from stock-price predictions [11,12] to the analysis of medical diseases [13]. In the past few years, several applications of machine-learning methods in the quantum domain have been reported [14][15][16], including state and unitary tomography [17][18][19][20][21][22][23][24][25], the design of quantum experiments [26][27][28][29][30][31][32], the validation of quantum technology [33][34][35], the identification of quantum features [36,37], and the adaptive control of quantum devices [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54]. Also, photonic platforms can be exploited for the realization of machine-learning protocols [55,56]...…”

Section: Introductionmentioning

confidence: 99%

Calibration of Multiparameter Sensors via Machine Learning at the Single-Photon Level

et al. 2021

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Calibration of sensors is a fundamental step in validating their operation. This can be a demanding task, as it relies on acquiring detailed modeling of the device, which can be aggravated by its possible dependence upon multiple parameters. Machine learning provides a handy solution to this issue, operating a mapping between the parameters and the device response, without needing additional specific information on its functioning. Here, we demonstrate the application of a neural-network-based algorithm for the calibration of integrated photonic devices depending on two parameters. We show that a reliable characterization is achievable by carefully selecting an appropriate network training strategy. These results show the viability of this approach as an effective tool for the multiparameter calibration of sensors characterized by complex transduction functions. Furthermore, the approach is proven to be versatile and promising for mass production, as the same neural network is able to calibrate different devices that have the same structure.

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Neural-Network Heuristics for Adaptive Bayesian Quantum Estimation

Cited by 49 publications

References 41 publications

Optimal Scheme for Quantum Metrology

Optimal Scheme for Quantum Metrology

Emergence of biased errors in imperfect photonic circuits

Calibration of Multiparameter Sensors via Machine Learning at the Single-Photon Level

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