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arXiv:2003.01605 · Physical Review Research

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

Valentin Gebhart, Martin Bohmann

Published May 11, 2020• 19 citations
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Abstract

This work uses the supervised learning of a deep neural network to identify nonclassicality of light detected through multiplexed measurement schemes. The network learns to correctly classify different classical and nonclassical states, outperforms other indicators of nonclassicality for a wide parameter range and also identifies nonclassicality for states outside the training set.

DOI
10.1103/physrevresearch.2.023150

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This work uses the supervised learning of a deep neural network to identify nonclassicality of light detected through multiplexed measurement schemes. The network learns to correctly classify different classical and nonclassical states, outperforms other indicators of nonclassicality for a wide parameter range and also identifies nonclassicality for states outside the training set.
Nonclassicality, defined in the quantum optical sense, serves as a resource for photon-based quantum technologies. Therefore, certifying the nonclassicality of a quantum state is crucial for gauging its potential for quantum advantage. However, traditional nonclassicality witnesses that assume perfect knowledge of the witness observables often fail in realistic scenarios with limited statistics and finite-resolution photon detectors. Furthermore, these witnesses do not exploit the fact that certain states are unlikely to be observed in a given experiment. Here, we train a variational model to distinguish classical from nonclassical states using finitely many measurement samples of multimode quantum states that are probed with different photon-number-resolving detection schemes. The learned decision rule is then an indicator of nonclassicality, tailored to a given set of physically relevant states. Our approach is both data-driven and interpretable in the sense that the learned analytical decision rule can be extracted. Training the model on experimental data measured with (i) a superconducting nanowire single-photon detector and (ii) a time-bin multiplexing detection scheme demonstrates the versatility of the approach, paving the way for efficient nonclassicality detection.
We introduce a general parameter QPB{Q}_{\mathrm{PB}}QPB​ that provides an experimentally accessible nonclassicality measure for light. The parameter is quantified by the click statistics obtained from on-off detectors in a general multiplexing detection setup. Sub-Poisson-binomial statistics, observed by QPB<0{Q}_{\mathrm{PB}}<0QPB​<0, indicates that a given state of light is nonclassical. Our parameter replaces the binomial parameter QB{Q}_{\mathrm{B}}QB​ for more general cases, where any unbalance among the multiplexed modes is allowed, thus enabling the use of arbitrary multiplexing schemes. The significance of the parameter QPB{Q}_{\mathrm{PB}}QPB​ is theoretically examined in a measurement setup that only consists of a ring resonator and a single on-off detector. The proposed setup exploits minimal experimental resources and is geared towards a fully integrated quantum nanophotonic circuit. The results show that nonclassical features remain noticeable even in the presence of significant losses, rendering our nonclassicality test more practical and sufficiently flexible to be used in various nanophotonic platforms.
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Deep learning have paved the way for scientists to achieve great technical feats. In an endeavor to hone and perfect these techniques, quantum deep learning is a promising and important tool to utilize to the fullest. Using the techniques of deep learning and supervised learning in a quantum framework, we are able to propose a quantum convolutional neural network and showcase its implementation. Using the techniques of deep learning and supervised learning in a quantum framework, we are able to propose a quantum convolutional neural network and showcase its implementation. We keep our focus on training of the ten qubits system in a way so that it can learn from labeling of the breast cell data of Wisconsin breast cancer database and optimize the circuit parameters to obtain the minimum error. Through our study, we also have showcased that quantum convolutional neural network can outperform its classical counterpart not only in terms of accuracy but also in the aspect of better time complexity.