Tootfinder

Hybrid Quantum-Classical Policy Gradient for Adaptive Control of Cyber-Physical Systems: A Comparative Study of VQC vs. MLP
Aueaphum Aueawatthanaphisut, Nyi Wunna Tun
https://arxiv.org/abs/2510.06010

Hybrid Quantum-Classical Policy Gradient for Adaptive Control of Cyber-Physical Systems: A Comparative Study of VQC vs. MLP
The comparative evaluation between classical and quantum reinforcement learning (QRL) paradigms was conducted to investigate their convergence behavior, robustness under observational noise, and computational efficiency in a benchmark control environment. The study employed a multilayer perceptron (MLP) agent as a classical baseline and a parameterized variational quantum circuit (VQC) as a quantum counterpart, both trained on the CartPole-v1 environment over 500 episodes. Empirical results dem…

On the sensitivity of different galaxy properties to warm dark matter
Bel\'en Costanza, Bonny Y. Wang, Francisco Villaescusa-Navarro, Alex M. Garcia, Jonah C. Rose, Mark Vogelsberger, Paul Torrey, Arya Farahi, Xuejian Shen, Ilem Leisher
https://arxiv.org/abs/2510.05037

On the sensitivity of different galaxy properties to warm dark matter
We study the impact of warm dark matter (WDM) particle mass on galaxy properties using 1,024 state-of-the-art cosmological hydrodynamical simulations from the DREAMS project. We begin by using a Multilayer Perceptron (MLP) coupled with a normalizing flow to explore global statistical descriptors of galaxy populations, such as the mean, standard deviation, and histograms of 14 galaxy properties. We find that subhalo gas mass is the most informative feature for constraining the WDM mass, achievin…