Neural-network approach for identifying nonclassicality from click-counting data

Gebhart, Valentin; Bohmann, Martin

doi:10.1103/physrevresearch.2.023150

Cited by 22 publications

(18 citation statements)

References 61 publications

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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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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…In recent years, the problem of the classification of unstructured and complex data was increasingly addressed with the help of machine learning techniques [34]. In the quantum domain, a wide range of challenges was tackled using various different forms of machine learning, see, e.g., [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50][51][52], and Ref. [53] for a review.…”

Section: Introductionmentioning

confidence: 99%

“…[53] for a review. In particular, machine learning tools have been applied to the identification of nonclassicality [49,50]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Identifying nonclassicality from experimental data using artificial neural networks

Gebhart,

Bohmann,

Weiher

et al. 2021

Preprint

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The fast and accessible verification of nonclassical resources is an indispensable step towards a broad utilization of continuous-variable quantum technologies. Here, we use machine learning methods for the identification of nonclassicality of quantum states of light by processing experimental data obtained via homodyne detection. For this purpose, we train an artificial neural network to classify classical and nonclassical states from their quadrature-measurement distributions. We demonstrate that the network is able to correctly identify classical and nonclassical features from real experimental quadrature data for different states of light. Furthermore, we show that nonclassicality of some states that were not used in the training phase is also recognized. Circumventing the requirement of the large sample sizes needed to perform homodyne tomography, our approach presents a promising alternative for the identification of nonclassicality for small sample sizes, indicating applicability for fast sorting or direct monitoring of experimental data.

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“…Recently, there has been an enormous interest in employing artificial neural networks to boost the functionality and robustness of quantum technologies [27][28][29][30][31][32]. In the field of photonics, there has been extensive research devoted to developing artificial neural networks for the implementation of novel optical instruments [33][34][35]. Indeed, convolutional neural networks (CNNs) have enabled the demonstration of new imaging schemes working at the single-photon level [17,36,37].…”

mentioning

confidence: 99%

High-dimensional encryption in optical fibers using machine learning

Lollie¹,

Mostafavi²,

Bhusal³

et al. 2021

Preprint

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The ability to engineer the spatial wavefunction of photons has enabled a variety of quantum protocols for communication, sensing, and information processing. These protocols exploit the high dimensionality of structured light enabling the encodinng of multiple bits of information in a single photon, the measurement of small physical parameters, and the achievement of unprecedented levels of security in schemes for cryptography. Unfortunately, the potential of structured light has been restrained to free-space platforms in which the spatial profile of photons is preserved. Here, we make an important step forward to using structured light for fiber optical communication. We introduce a smart high-dimensional encryption protocol in which the propagation of spatial modes in multimode fibers is used as a natural mechanism for encryption. This provides a secure communication channel for data transmission. The information encoded in spatial modes is retrieved using artificial neural networks, which are trained from the intensity distributions of experimentally detected spatial modes. Our on-fiber communication platform allows us to use spatial modes of light for high-dimensional bit-by-bit and byte-by-byte encoding. This protocol enables one to recover messages and images with almost perfect accuracy. Our smart protocol for high-dimensional optical encryption in optical fibers has key implications for quantum technologies relying on structured fields of light, particularly those that are challenged by free-space propagation.

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Neural-network approach for identifying nonclassicality from click-counting data

Cited by 22 publications

References 61 publications

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

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

Identifying nonclassicality from experimental data using artificial neural networks

High-dimensional encryption in optical fibers using machine learning

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