Accuracy of Entanglement Detection via Artificial Neural Networks and Human-Designed Entanglement Witnesses

Roik, Jan; Bartkiewicz, Karol; Cernoch, A; Lemr, Karel

doi:10.1103/physrevapplied.15.054006

Cited by 17 publications

(10 citation statements)

References 42 publications

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“…Beyond the methods presented in this review, it is fair and also worth mentioning novel techniques that instead employ machine learning to reduce the verification requirements. In fact, the use of machine learning for quantum applications is in general experiencing rapid progress and proving useful in tasks like entanglement detection using neural networks [107,108] or unsupervised learning, [109] and quantum state tomography using neural networks. [19] It is also relevant that a comparable method (to SQST) for estimating elements of a density matrix exists in the continuous variable (CV) regime.…”

Section: Discussionmentioning

confidence: 99%

Quantum Verification and Estimation with Few Copies

et al. 2022

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As quantum technologies advance, the ability to generate increasingly large quantum states has experienced rapid development. In this context, the verification and estimation of large entangled systems represent one of the main challenges in the employment of such systems for reliable quantum information processing. Though the most complete technique is undoubtedly full tomography, the inherent exponential increase of experimental and post‐processing resources with system size makes this approach infeasible even at moderate scales. For this reason, there is currently an urgent need to develop novel methods that surpass these limitations. This review article presents novel techniques focusing on a fixed number of resources (sampling complexity), and thus prove suitable for systems of arbitrary dimension. Specifically, a probabilistic framework requiring at best only a single copy for entanglement detection is reviewed, together with the concept of selective quantum state tomography, which enables the estimation of arbitrary elements of an unknown state with a number of copies that is low and independent of the system's size. These hyper‐efficient techniques define a dimensional demarcation for partial tomography and open a path for novel applications.

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Section: Discussionmentioning

confidence: 99%

Quantum Verification and Estimation with Few Copies

et al. 2022

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“…Beyond the methods presented in this review, it is fair and also worth mentioning novel techniques that instead employ machine learning to reduce the verification requirements. In fact, the use of machine learning for quantum applications is in general experiencing rapid progress and proving useful in tasks like entanglement detection using neural networks [104,105] or unsupervised learning [106], and quantum state tomography using neural networks [19]. It is also pertinent that a comparable method (to SQST) for estimating elements of a density matrix exists in the continuous variable (CV) regime.…”

Section: Discussionmentioning

confidence: 99%

Quantum verification and estimation with few copies

Morris,

Saggio,

Gočanin

et al. 2021

Preprint

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As quantum technologies advance, the ability to generate increasingly large quantum states has experienced rapid development. In this context, the verification of large entangled systems represents one of the main challenges in the employment of such systems for reliable quantum information processing. Though the most complete technique is undoubtedly full tomography, the inherent exponential increase of experimental and postprocessing resources with system size makes this approach unfeasible even at moderate scales. For this reason, there is currently an urgent need to develop novel methods that surpass these limitations. This review article presents novel techniques focusing on a fixed number of resources (sampling complexity), and thus prove suitable for systems of arbitrary dimension. Specifically, a probabilistic framework requiring at best only a single copy for entanglement detection is reviewed, together with the concept of shadow and selective quantum state tomography, which enables the estimation of arbitrary elements of an unknown state with a number of copies that is independent of the system's size. These hyper-efficient techniques define a dimensional demarcation for partial tomography and open a path for novel applications.

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“…In addition, other machine learning techniques such as support vector machines and decision trees can also serve the purpose of building an entanglement-separability classifier 29 . Comparative studies between artificial neural networks and witness-based methods for classifying quantum states have demonstrated that artificial neural networks perform significantly better than witness-based methods 30 . Unsupervised learning techniques have also been studied for quantum state classification along with supervised learning.…”

Section: Introductionmentioning

confidence: 99%

Entanglement detection with artificial neural networks

Asif

Khalid

Khan

et al. 2023

Sci Rep

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Quantum entanglement is one of the essential resources involved in quantum information processing tasks. However, its detection for usage remains a challenge. The Bell-type inequality for relative entropy of coherence serves as an entanglement witness for pure entangled states. However, it does not perform reliably for mixed entangled states. This paper constructs a classifier by employing the relationship between coherence and entanglement for supervised machine learning methods. This method encodes multiple Bell-type inequalities for the relative entropy of coherence into an artificial neural network to detect the entangled and separable states in a quantum dataset.

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Accuracy of Entanglement Detection via Artificial Neural Networks and Human-Designed Entanglement Witnesses

Cited by 17 publications

References 42 publications

Quantum Verification and Estimation with Few Copies

Quantum Verification and Estimation with Few Copies

Quantum verification and estimation with few copies

Entanglement detection with artificial neural networks

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