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Extract the Degradation Information in Squeezed States with Machine Learning.

Hsien-Yi Hsieh¹, Yi-Ru Chen¹, Hsun-Chung Wu¹

¹Institute of Photonics Technologies, National Tsing Hua University, Hsinchu 30013, Taiwan.

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Summary

This summary is machine-generated.

Machine learning accelerates quantum state tomography for continuous variables, enabling precise density matrix reconstruction in under a second. This method accurately characterizes squeezed light purity degradation, even in noisy environments.

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Area of Science:

Quantum Optics
Quantum Information Science
Machine Learning

Background:

Understanding decoherence is crucial for harnessing quantum noise squeezing.
Accurate quantum state tomography is essential for characterizing quantum states.

Purpose of the Study:

To develop a fast, robust, and precise quantum state tomography method for continuous variables.
To utilize machine learning, specifically convolutional neural networks, for this task.
To analyze decoherence effects on squeezed light purity.

Main Methods:

Implemented a convolutional neural network architecture for quantum state tomography.
Used experimentally measured data from balanced homodyne detectors.
Trained the neural network with squeezed vacuum and squeezed thermal states.

Main Results:

Achieved density matrix reconstruction in less than one second, outperforming maximum likelihood estimation.
Maintained high fidelity (0.99) even with high antisqueezing levels (>20 dB).
Unveiled degradation information in both low (12 dB) and high (18 dB) noisy scenarios.

Conclusions:

Neural network-enhanced quantum state tomography offers a significant advancement in speed and precision.
The method provides physical insights into quantum state features with minimal measurements.
Paves the way for real-time exploration of large-scale quantum systems.