Devin Seyler | Searcharxiv

arXiv2023

Noise-immune quantum correlations of intense light

Lasers with high intensity generally exhibit strong intensity fluctuations far above the shot-noise level. Taming this noise is pivotal to a wide range of applications, both classical and quantum. Here, we demonstrate the creation of intense light with quantum levels of noise even when starting from inputs with large amounts of excess noise. In particular, we demonstrate how intense squeezed light with intensities approaching 0.1 TW/cm^2, but noise at or below the shot noise level, can be produced from noisy inputs associated with high-power amplified laser sources (an overall noise-reduction of 30-fold). Based on a new theory of quantum noise in multimode systems, we show that the ability to generate quantum light from noisy inputs results from multimode quantum correlations, which maximally decouple the output light from the dominant noise channels in the input light. As an example, we demonstrate this effect for femtosecond pulses in nonlinear fibers, but the noise-immune correlations that enable our results are generic to many other nonlinear systems in optics and beyond.

quant-ph↗

arXiv2021

NEORL: NeuroEvolution Optimization with Reinforcement Learning

We present an open-source Python framework for NeuroEvolution Optimization with Reinforcement Learning (NEORL) developed at the Massachusetts Institute of Technology. NEORL offers a global optimization interface of state-of-the-art algorithms in the field of evolutionary computation, neural networks through reinforcement learning, and hybrid neuroevolution algorithms. NEORL features diverse set of algorithms, user-friendly interface, parallel computing support, automatic hyperparameter tuning, detailed documentation, and demonstration of applications in mathematical and real-world engineering optimization. NEORL encompasses various optimization problems from combinatorial, continuous, mixed discrete/continuous, to high-dimensional, expensive, and constrained engineering optimization. NEORL is tested in variety of engineering applications relevant to low carbon energy research in addressing solutions to climate change. The examples include nuclear reactor control and fuel cell power production. The results demonstrate NEORL competitiveness against other algorithms and optimization frameworks in the literature, and a potential tool to solve large-scale optimization problems. More examples and benchmarking of NEORL can be found here: https://neorl.readthedocs.io/en/latest/index.html

cs.NE↗