Correlation-pattern-based Continuous-variable Entanglement Detection through Neural Networks
Entanglement in continuous-variable non-Gaussian states provides irreplaceable advantages in many quantum information tasks. However, the sheer amount of information in such states grows exponentially and makes a full characterization impossible. Here, we develop a neural network that allows us to u...
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Main Authors | , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
31.10.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Entanglement in continuous-variable non-Gaussian states provides
irreplaceable advantages in many quantum information tasks. However, the sheer
amount of information in such states grows exponentially and makes a full
characterization impossible. Here, we develop a neural network that allows us
to use correlation patterns to effectively detect continuous-variable
entanglement through homodyne detection. Using a recently defined stellar
hierarchy to rank the states used for training, our algorithm works not only on
any kind of Gaussian state but also on a whole class of experimentally
achievable non-Gaussian states, including photon-subtracted states. With the
same limited amount of data, our method provides higher accuracy than usual
methods to detect entanglement based on maximum-likelihood tomography.
Moreover, in order to visualize the effect of the neural network, we employ a
dimension reduction algorithm on the patterns. This shows that a clear boundary
appears between the entangled states and others after the neural network
processing. In addition, these techniques allow us to compare different
entanglement witnesses and understand their working. Our findings provide a new
approach for experimental detection of continuous-variable quantum correlations
without resorting to a full tomography of the state and confirm the exciting
potential of neural networks in quantum information processing. |
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DOI: | 10.48550/arxiv.2310.20570 |