Ultra-stable and high-performance squeezed vacuum source enabled via artificial intelligence control

Squeezing states are crucial for advancing quantum metrology beyond the classical limit. Despite this, generating high-performance squeezed light with long-term stability remains a challenge due to system complexity and quantum fragility. We experimentally achieved a record-breaking squeezing level of 4.3 decibels (lossless, 5.9 decibels) using polarization self-rotation (PSR) in atomic vapor, maintaining stability for hours despite environmental disturbances. Overcoming the limitations of the PSR theory model's optimization guidance, which arises from the mutual interference of multiple parameters at this squeezing level, we developed an artificial intelligence (AI) control (AIC) system that harnesses deep learning to discern and manage these complex relationships, thereby enabling self-adapted to external environments. This integrated approach represents a concrete step for the actual application of quantum metrology and information processing, illustrating the synergy between AI and fundamental science in breaking complexity constraints.

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Publication Details

Journal
Science Advances
Published
2025-05-02
DOI
https://doi.org/10.1126/sciadv.adu4888
Citations
4
Primary Topic
Atomic and Subatomic Physics Research
Type
article
Field-Weighted Citation Impact
4.99
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Ultra-stable and high-performance squeezed vacuum source enabled via artificial intelligence control

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Ultra-stable and high-performance squeezed vacuum source enabled via artificial intelligence control

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article en
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Abstract

Squeezing states are crucial for advancing quantum metrology beyond the classical limit. Despite this, generating high-performance squeezed light with long-term stability remains a challenge due to system complexity and quantum fragility. We experimentally achieved a record-breaking squeezing level of 4.3 decibels (lossless, 5.9 decibels) using polarization self-rotation (PSR) in atomic vapor, maintaining stability for hours despite environmental disturbances. Overcoming the limitations of the PSR theory model's optimization guidance, which arises from the mutual interference of multiple parameters at this squeezing level, we developed an artificial intelligence (AI) control (AIC) system that harnesses deep learning to discern and manage these complex relationships, thereby enabling self-adapted to external environments. This integrated approach represents a concrete step for the actual application of quantum metrology and information processing, illustrating the synergy between AI and fundamental science in breaking complexity constraints.

Science AdvancesVol. 11(18)
Hefei University of Technology (CN), East China Normal University (CN)
Openalex Percentile: Top 5%
Atomic and Subatomic Physics Research
4.99
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