Multimodal Deep Representation Learning for Quantum Cross-Platform Verification

Qian, Yang; Du, Yuxuan; He, Zhenliang; Hsieh, Min-Hsiu; Tao, Dacheng

doi:10.1103/physrevlett.133.130601

Phys. Rev. Lett.

2024

DOI: 10.1103/physrevlett.133.130601

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Multimodal Deep Representation Learning for Quantum Cross-Platform Verification

Yang Qian,

Yuxuan Du,

Zhenliang He

et al.

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Cited by 3 publications

References 63 publications

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Learning quantum properties from short-range correlations using multi-task networks

Wu,

Zhu,

Wang

et al. 2024

Nat Commun

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Learning quantum properties from short-range correlations using multi-task networks

Wu,

Zhu,

Wang

et al. 2024

Nat Commun

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Inertia of decomposable entanglement witnesses

Chen,

Jiang

2024

Phys. Scr.

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We investigate the inertia of decomposable entanglement witnesses (EWs). We show that the 2 × n and two-qutrit decomposable EWs have the same inertias as those of non-positive-transpose (NPT) EWs. We also show that if an m×n EW W has inertia (p, ap, mn- p - ap) with p ‒ 1, then for every integer b ∈ [0, ap], then we can ﬁnd an EW Wb such that In Wb = (p, b, mn - p - b). If W is a decomposable (resp. NPT) EW, then we can choose Wb as also a decomposable (resp. NPT) EW. We further show that the m × n decomposable EW with the maximum number of negative eigenvalues can be chosen as an NPT EW. Then we explicitly characterize the 2 × 3 EWs, and decomposable EWs P ? +Q with positive semideﬁnite matrices P of rank one and Q. We also show that a 2 × 4 non-decomposable EW has no inertia (3, 2, 3). Then we show some properties of a 2 × 4 non-decomposable EW of inertia (2, 3, 3), if it exists.

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Optical quantum sensing for agnostic environments via deep learning

Zhou,

Du,

Yin

et al. 2024

Phys. Rev. Research

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Optical quantum sensing promises measurement precision beyond classical sensors termed the Heisenberg limit (HL). However, conventional methodologies often rely on prior knowledge of the target system to achieve HL, presenting challenges in practical applications. Addressing this limitation, we introduce an innovative deep-learning-based quantum sensing scheme (DQS), enabling optical quantum sensors to attain HL in agnostic environments. DQS incorporates two essential components: a graph neural network (GNN) predictor and a trigonometric interpolation algorithm. Operating within a data-driven paradigm, DQS utilizes the GNN predictor, trained on offline data, to unveil the intrinsic relationships between the optical setups employed in preparing the probe state and the resulting quantum Fisher information (QFI) after interaction with the agnostic environment. This distilled knowledge facilitates the identification of optimal optical setups associated with maximal QFI. Subsequently, DQS employs a trigonometric interpolation algorithm to recover the unknown parameter estimates for the identified optical setups. Extensive experiments are conducted to investigate the performance of DQS under different settings up to eight photons. Our findings not only offer a different lens through which to accelerate optical quantum sensing tasks but also catalyze future research integrating deep learning and quantum mechanics. Published by the American Physical Society 2024

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Multimodal Deep Representation Learning for Quantum Cross-Platform Verification

Cited by 3 publications

References 63 publications

Learning quantum properties from short-range correlations using multi-task networks

Learning quantum properties from short-range correlations using multi-task networks

Inertia of decomposable entanglement witnesses

Optical quantum sensing for agnostic environments via deep learning

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