Experimental multiparameter quantum metrology in adaptive regime

Valeri, Mauro; Cimini, Valeria; Piacentini, Simone; Ceccarelli, Francesco; Polino, Emanuele; Hoch, Francesco; Bizzarri, Gabriele; Corrielli, Giacomo; Spagnolo, Nicolò; Osellame, Roberto; Sciarrino, Fabio

doi:10.1103/physrevresearch.5.013138

Cited by 14 publications

(12 citation statements)

References 87 publications

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“…We apply the Bayesian NN adaptive protocol to estimate three optical phases in an integrated four-arm interferometer. 17 The device is fabricated through the femtosecond laser writing (FLW) technique, 63 and all the optical phases can be tuned applying a voltage to various microheaters, patterned in a thin gold layer and placed onto the different arms of the interferometer. In particular, the interferometric phases under study are combined with two integrated quarter splitters (4 × 4 balanced beam splitters) that close the interferometer (see Fig.…”

Section: Extension To Real Scenarios: Multiparameter Estimationmentioning

confidence: 99%

“…Therefore, a calibration of the quantum device, based on the device physical model necessary for its optimal use, is not always feasible and often computationally and experimentally demanding. We apply the Bayesian NN adaptive protocol to estimate three optical phases in an integrated four-arm interferometer 17 . The device is fabricated through the femtosecond laser writing (FLW) technique, 63 and all the optical phases can be tuned applying a voltage to various microheaters, patterned in a thin gold layer and placed onto the different arms of the interferometer.…”

Section: Artificial Intelligence Quantum Metrologymentioning

confidence: 99%

“…One of the most employed methods, which assures the convergence to the ultimate precision bound, is to update the knowledge on the parameter posterior distribution through the Bayes rule after the use of each resource. [13][14][15][16][17] Therefore, adaptive methods generally require a precise characterization of the operation of the employed system in order to update properly the knowledge on the investigated parameters at each step of the protocol. Such requirement is still the bottleneck for the application of optimal adaptive protocols in most quantum sensing applications.…”

Section: Introductionmentioning

confidence: 99%

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Deep reinforcement learning for quantum multiparameter estimation

et al. 2023

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Estimation of physical quantities is at the core of most scientific research, and the use of quantum devices promises to enhance its performances. In real scenarios, it is fundamental to consider that resources are limited, and Bayesian adaptive estimation represents a powerful approach to efficiently allocate, during the estimation process, all the available resources. However, this framework relies on the precise knowledge of the system model, retrieved with a fine calibration, with results that are often computationally and experimentally demanding. We introduce a model-free and deep-learning-based approach to efficiently implement realistic Bayesian quantum metrology tasks accomplishing all the relevant challenges, without relying on any a priori knowledge of the system. To overcome this need, a neural network is trained directly on experimental data to learn the multiparameter Bayesian update. Then the system is set at its optimal working point through feedback provided by a reinforcement learning algorithm trained to reconstruct and enhance experiment heuristics of the investigated quantum sensor. Notably, we prove experimentally the achievement of higher estimation performances than standard methods, demonstrating the strength of the combination of these two black-box algorithms on an integrated photonic circuit. Our work represents an important step toward fully artificial intelligence-based quantum metrology.

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Section: Extension To Real Scenarios: Multiparameter Estimationmentioning

confidence: 99%

Section: Artificial Intelligence Quantum Metrologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep reinforcement learning for quantum multiparameter estimation

et al. 2023

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“…This optimization process can be done through different online and offline [19][20][21][22][23] protocols. One of the most implemented ones is developed within the Bayesian inference framework, and is based on updating the knowledge of the parameter value depending on the registered measurement outcome and on the setting of some control parameter [24][25][26][27] valid for both the qubit framework [28] and the continuous-variable one [29]. The advantage becomes particularly significant in the multiparameter framework where the interest relies on the simultaneous estimation of several parameters [30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Bayesian quantum estimation

Cimini,

Polino,

Valeri

et al. 2024

Quantum Sci. Technol.

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The quest for precision in parameter estimation is a fundamental task in different scientific areas. The relevance of this problem thus provided the motivation to develop methods for the application of quantum resources to estimation protocols. Within this context, Bayesian estimation offers a complete framework for optimal quantum metrology techniques, such as adaptive protocols. However, the use of the Bayesian approach requires extensive computational resources, especially in the multiparameter estimations that represent the typical operational scenario for quantum sensors. Hence, the requirement to characterize protocols implementing Bayesian estimations can become a significant challenge. This work focuses on the crucial task of robustly benchmarking the performances of these protocols in both single and multiple-parameter scenarios. By comparing different figures of merits, evidence is provided in favor of using the median of the quadratic error in the estimations in order to mitigate spurious effects due to the numerical discretization of the parameter space, the presence of limited data, and numerical instabilities. These results, providing a robust and reliable characterization of Bayesian protocols, find natural applications to practical problems within the quantum estimation framework.

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“…Alternative approaches have been implemented for ab-initio phase estimations, sampling multiple times the investigated phase shift 23 through adaptive and non-adaptive multi-pass strategies 6 , achieving Heisenberg scaling performances in an entanglement-free fashion. The estimation of one or multiple parameters with several approaches in different contexts has been studied in the last years [24][25][26][27][28][29][30][31][32] . However, one of the main challenges is to maintain the Heisenberg scaling when increasing the number of dedicated resources.…”

Section: Introductionmentioning

confidence: 99%

Experimental metrology beyond the standard quantum limit for a wide resources range

et al. 2023

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Adopting quantum resources for parameter estimation discloses the possibility to realize quantum sensors operating at a sensitivity beyond the standard quantum limit. Such an approach promises to reach the fundamental Heisenberg scaling as a function of the employed resources N in the estimation process. Although previous experiments demonstrated precision scaling approaching Heisenberg-limited performances, reaching such a regime for a wide range of N remains hard to accomplish. Here, we show a method that suitably allocates the available resources permitting them to reach the same power law of Heisenberg scaling without any prior information on the parameter. We demonstrate experimentally such an advantage in measuring a rotation angle. We quantitatively verify sub-standard quantum limit performances for a considerable range of N (O(30,000)) by using single-photon states with high-order orbital angular momentum, achieving an error reduction, in terms of the obtained variance, >10 dB below the standard quantum limit. Such results can be applied to different scenarios, opening the way to the optimization of resources in quantum sensing.

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Experimental multiparameter quantum metrology in adaptive regime

Cited by 14 publications

References 87 publications

Deep reinforcement learning for quantum multiparameter estimation

Deep reinforcement learning for quantum multiparameter estimation

Benchmarking Bayesian quantum estimation

Experimental metrology beyond the standard quantum limit for a wide resources range

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