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Correlation-Pattern-Based Continuous Variable Entanglement Detection through Neural Networks.
Xiaoting Gao1,2, Mathieu Isoard2, Fengxiao Sun1,3
1State Key Laboratory for Mesoscopic Physics, School of Physics, Frontiers Science Center for Nano-optoelectronics, & Collaborative Innovation Center of Quantum Matter, Peking University, Beijing 100871, China.
Physical Review Letters
|June 15, 2024
Summary
We developed a neural network to detect quantum entanglement in complex states using correlation patterns. This method offers higher accuracy than traditional techniques, even with limited data.
Area of Science:
- Quantum Information Science
- Quantum Computing
- Machine Learning Applications
Background:
- Continuous-variable (CV) non-Gaussian states offer significant advantages in quantum information tasks.
- Characterizing these states is challenging due to exponential information growth.
Purpose of the Study:
- To develop a neural network for effective detection of CV entanglement.
- To enable entanglement detection without full state tomography.
Main Methods:
- Utilized a neural network trained on correlation patterns from homodyne detection.
- Employed a stellar hierarchy for ranking training states.
- Applied dimension reduction algorithms for visualization.
Main Results:
- The neural network accurately detects entanglement in both Gaussian and non-Gaussian states.
- Achieved higher accuracy than maximum-likelihood tomography with limited data.
- Visualization revealed a clear boundary between entangled and non-entangled states.
Conclusions:
- The neural network provides an efficient method for experimental detection of CV quantum correlations.
- Demonstrated the potential of neural networks in quantum information processing.
- Facilitated comparison and understanding of different entanglement witnesses.
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