Generation of 12 dB squeezed light from a waveguide optical parametric amplifier using a machine-learning-controlled spatial light modulator

TL;DR

This paper reports the generation of 12.1 dB of squeezed light from a PPLN waveguide OPA, achieved by using a machine-learning-optimized spatial light modulator to minimize mode mismatch loss.

Contribution

It introduces a machine-learning approach to optimize the spatial light modulator, significantly improving squeezing levels in a waveguide OPA system.

Findings

Achieved 12.1 dB squeezing level.

Reduced mode mismatch loss using machine learning.

Enhanced squeezing performance with a double-reflection SLM configuration.

Abstract

We demonstrate the generation of $12.1 \pm 0.2$ dB squeezed light from a periodically poled lithium niobate (PPLN) waveguide optical parametric amplifier (OPA). While single-pass OPAs offer squeezed light with THz-order bandwidths, loss from spatial mode mismatch between the squeezed light and the local oscillator (LO) previously capped the squeezing level at $\sim$10 dB [K. Hirota et al., Opt. Express 34, 7958 (2026)]. In this work, we minimize this loss by introducing a machine-learning-optimized spatial light modulator (SLM) in the path of the LO. Specifically, we employed a double-reflection configuration to increase the spatial degrees of freedom, and directly used the measured squeezing level as the optimization's objective function.