Experimental Simultaneous Learning of Multiple Nonclassical Correlations

Yang, Ming; Ren, Changliang; Ma, Yuxin; Xiao, Ya; Ye, Xiang-Jun; Song, Lulu; Xu, Jin‐Shi; Yung, Man‐Hong; Li, Chuan‐Feng; Guo, Guang-Can

doi:10.1103/physrevlett.123.190401

Cited by 38 publications

(21 citation statements)

References 33 publications

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“…Previous work on using machine learning for the separability problem has been focused either having the machine choose good measurements and then using an existing entanglement criteria [25,26] , or on viewing the task as a classification problem [27][28][29][30][31][32][33]. For classification, typically a training set is constructed where quantum states are labeled as separable or entangled.…”

Section: Related Workmentioning

confidence: 99%

Building separable approximations for quantum states via neural networks

Girardin,

Brunner,

Kriváchy

2021

Preprint

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Section: Related Workmentioning

confidence: 99%

Building separable approximations for quantum states via neural networks

Girardin,

Brunner,

Kriváchy

2021

Preprint

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“…entanglement has received a significant attention in the context of machine learning [34][35][36][37][38][39][40][41][42][43]. The proposed methods are quite efficient and provide high enough classification accuracy, however they mostly require a complicated set of experimental data.…”

Section: Introductionmentioning

confidence: 99%

Learning entanglement breakdown as a phase transition by confusion

Gavreev,

Mastiukova,

Kiktenko

et al. 2022

Preprint

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Quantum technologies require methods for preparing and manipulating entangled multiparticle states. However, the problem of determining whether a given quantum state is entangled or separable is known to be an NP-hard problem in general, and even the task of detecting entanglement breakdown for a given class of quantum states is difficult. In this work, we develop an approach for revealing entanglement breakdown using a machine learning technique, which is known as 'learning by confusion'. We consider a family of quantum states, which is parameterized such that there is a single critical value dividing states within this family on separate and entangled. We demonstrate the 'learning by confusion' scheme allows determining the critical value. Specifically, we study the performance of the method for the two-qubit, two-qutrit, and two-ququart entangled state, where the standard entanglement measures do not work efficiently. In addition, we investigate the properties of the local depolarization and the generalized amplitude damping channel in the framework of the confusion scheme. Within our approach and setting the parameterization of special trajectories to construct W shapes, we obtain an entanglement-breakdown 'phase diagram' of a quantum channel, which indicates regions of entangled (separable) states and the entanglement-breakdown region. Then we extend the way of using the 'learning by confusion' scheme for recognizing whether an arbitrary given state is entangled or separable. We show that the developed method provides correct answers for a variety of states, including entangled states with positive partial transpose (PPT). We also present a more practical version of the method, which is suitable for studying entanglement breakdown in noisy intermediate-scale quantum (NISQ) devices. We demonstrate its performance using an available cloud-based IBM quantum processor. Separable states ''Phase II'' Entangled states ''Phase I'' Maximally entangled states Maximally mixed state Critical point

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“…Especially, the task of quantum entanglement detection could be formulated as a binary classification problem. As a consequence, various classical neural nets, trained with both entangled and separable samples, have been constructed to solve this problem via supervised learning [22][23][24]. However, the supervised training method requires a large pre-labelled dataset.…”

Section: Introductionmentioning

confidence: 99%

Detecting quantum entanglement with unsupervised learning

Chen,

Pan,

Zhang

et al. 2021

Preprint

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Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient and scalable way to detecting these useful features especially for high-dimensional quantum systems. In this work, we exploit the convexity of normal samples without quantum features and design an unsupervised machine learning method to detect the presence of quantum features as anomalies. Particularly, given the task of entanglement detection, we propose a complex-valued neural network composed of pseudo-siamese network and generative adversarial net, and then train it with only separable states to construct non-linear witnesses for entanglement. It is shown via numerical examples, ranging from 2-qubit to 10-qubit systems, that our network is able to achieve high detection accuracy with above 97.5% on average. Moreover, it is capable of revealing rich structures of entanglement, such as partial entanglement among subsystems. Our results are readily applicable to the detection of other quantum resources such as Bell nonlocality and steerability, indicating that our work could provide a powerful tool to extract quantum features hidden in high-dimensional quantum data.

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Experimental Simultaneous Learning of Multiple Nonclassical Correlations

Cited by 38 publications

References 33 publications

Building separable approximations for quantum states via neural networks

Building separable approximations for quantum states via neural networks

Learning entanglement breakdown as a phase transition by confusion

Detecting quantum entanglement with unsupervised learning

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