Tootfinder

Automated Evolutionary Optimization for Resource-Efficient Neural Network Training
Ilia Revin, Leon Strelkov, Vadim A. Potemkin, Ivan Kireev, Andrey Savchenko
https://arxiv.org/abs/2510.09566

Automated Evolutionary Optimization for Resource-Efficient Neural Network Training
There are many critical challenges in optimizing neural network models, including distributed computing, compression techniques, and efficient training, regardless of their application to specific tasks. Solving such problems is crucial because the need for scalable and resource-efficient models is increasing. To address these challenges, we have developed a new automated machine learning (AutoML) framework, Parameter Efficient Training with Robust Automation (PETRA). It applies evolutionary op…

It is too expensive. Also, there is not enough competition.
https://flip.it/xnptCg

Power corrections to the production of a prompt photon in association with a jet in the $N$-jettiness slicing scheme at NLO QCD
Prem Agarwal, Kirill Melnikov, Ivan Pedron
https://arxiv.org/abs/2510.03097

Power corrections to the production of a prompt photon in association with a jet in the $N$-jettiness slicing scheme at NLO QCD
We compute the next-to-leading-power corrections in the $N$-jettiness variable to the production of a prompt photon and a jet at next-to-leading order in perturbative QCD in the $q \bar q$ annihilation channel. We employ the $k_\perp$ jet algorithm and assume that the $N$-jettiness value divided by the jet transverse momentum is the smallest parameter in the problem; in particular it should be small compared to the jet radius $R$.

A Hybrid Quantum-AI Framework for Protein Structure Prediction on NISQ Devices
Yuqi Zhang, Yuxin Yang, Feixiong Chen, Cheng-Chang Lu, Nima Saeidi, Samuel L. Volchenboum, Junhan Zhao, Siwei Chen, Weiwen Jiang, Qiang Guan
https://arxiv.org/abs/2510.06413

A Hybrid Quantum-AI Framework for Protein Structure Prediction on NISQ Devices
Variational quantum algorithms provide a direct, physics-based approach to protein structure prediction, but their accuracy is limited by the coarse resolution of the energy landscapes generated on current noisy devices. We propose a hybrid framework that combines quantum computation with deep learning, formulating structure prediction as a problem of energy fusion. Candidate conformations are obtained through the Variational Quantum Eigensolver (VQE) executed on IBM's 127-qubit superconducting…