TMCnet

MicroAlgo Inc. Develops Quantum Architecture Search (QAS) Technology to Enhance VQA Robustness and Trainability, Optimizing the Potential of Quantum Computing Devices

MicroAlgo QAS, by optimizing quantum circuit architectures, can significantly enhance the robustness of VQA in various noisy environments. By automatically searching for suitable circuit designs, QAS ...
IEEE

Reinforcement Learning Based Joint Optimization of Active RIS Reflection and Power Allocation for Cognitive Radar Networks

Abstract: Cognitive radar networks have emerged as a key enabler for intelligent unmanned aerial vehicle (UAV) and vehicular sensing, providing adaptive perception, dynamic resource management, and ...
Hosted on MSN

Machine learning cuts testing time in metal laser 3D printing

Researchers at Empa have developed machine learning algorithms that reduce preliminary testing in laser-based metal additive manufacturing by about two-thirds without compromising quality. Using ...
TMCnet

MicroAlgo Inc. Develops Optimal Precise Quantum Query Algorithm Based on Sum-of-Squares Representation Form of Boolean Functions

MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the proposal of a new approach to solving the Boolean function query problem. This framework starts from the ...

New AI framework autonomously optimizes training data, architectures and algorithms — outperforming human baselines

EVOLVE, an agentic framework that autonomously optimizes AI training data, model architectures, and learning algorithms — ...
Scientific Research Publishing

Reinforcement Learning Based Optimization of Sleep Mood Circadian Dynamics in Bipolar Disorder: A Simulation Study ()

1 Department of Neuroscience, Institute of Psychopathology, Rome, Italy. 2 Department of Computer Engineering (AI), University of Genova, Genova, Italy. Bipolar disorder (BD) is closely intertwined ...
Frontiers

Optimizing hip exoskeleton assistance pattern based on machine learning and simulation algorithms: a personalized approach to metabolic cost reduction

Using data from ten healthy adults, we trained a Gradient Boosting (GB) surrogate model to predict normalized metabolic cost as a function of Peak Magnitude and End ...
IEEE

Research on Deep Learning-Based Optimization Algorithms for IoT Communication

Abstract: The rapid proliferation of Internet of Things (IoT) devices has brought new challenges in designing efficient, adaptive, and communication-aware optimization strategies under strict resource ...