Results 41 to 50 of about 23,653 (255)
Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization
Advanced Engineering Materials, EarlyView.Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier +17 more
wiley +1 more source
NetKet: A machine learning toolkit for many-body quantum systems
SoftwareX, 2019 We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and flexible implementation of neural-network quantum states, which are used ...
Giuseppe Carleo +18 more
doaj +1 more source
Experimental quantum end-to-end learning on a superconducting processor
npj Quantum Information, 2023 Machine learning can be enhanced by a quantum computer via its inherent quantum parallelism. In the pursuit of quantum advantages for machine learning with noisy intermediate-scale quantum devices, it was proposed that the learning model can be designed ...
Xiaoxuan Pan +12 more
doaj +1 more source
The PRIMA Thesaurus for Materials Science and Engineering
Advanced Engineering Materials, EarlyView.The PRIMA Thesaurus is a structured vocabulary designed to improve how materials science data is described and shared. Developed with input from multiple experts, it enables clear documentation of research workflows, data exchange, and reuse across platforms.
Rossella Aversa +8 more
wiley +1 more source
New Journal of PhysicsThe standard model of quantum computation is based on quantum circuits, where the number and quality of the quantum gates composing the circuit influence the runtime and fidelity of the computation. The fidelity of the decomposition of quantum algorithms,
Aritra Sarkar +7 more
doaj +1 more source
Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning
2025 IEEE International Symposium on Circuits and Systems (ISCAS)In ...
Jun Qi 0002 +3 more
openaire +2 more sources
Advanced Engineering Materials, EarlyView.Knowledge‐based atomistic workflows are presented for mechanical and thermodynamic properties. By coupling modular simulations with ontology‐aligned metadata and provenance, Fe case studies on elastic behavior, defects, thermal properties, and Hall–Petch strengthening reveal how FAIR, queryable, and reusable simulation data can be generated. Mechanical
Abril Azócar Guzmán +5 more
wiley +1 more source
An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials
Advanced Engineering Materials, EarlyView.An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut +16 more
wiley +1 more source
Variational Quantum Circuits for Deep Reinforcement Learning
IEEE Access, 2020 The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains.
Samuel Yen-Chi Chen +5 more
doaj +1 more source
Remote Sensing, 2022 A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers.
Riyaaz Uddien Shaik +2 more
doaj +1 more source