Results 51 to 60 of about 34,577 (263)

Toward Knowledge‐Based Workflows: A Semantic Approach to Atomistic Simulations for Mechanical and Thermodynamic Properties

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, Hoang‐Thien Luu, Sarath Menon, Tilmann Hickel, Nina Merkert, Stefan Sandfeld   +5 more
wiley   +1 more source

Parameterized quantum circuits as universal generative models for continuous multivariate distributions

npj Quantum Information
Parameterized quantum circuits are a key component of quantum machine learning models for regression, classification, and generative tasks. Quantum Circuit Born machines produce discrete distributions over bitstrings whose length is exactly the number of
Alice Barthe, Michele Grossi, Sofia Vallecorsa, Jordi Tura, Vedran Dunjko   +4 more
doaj   +1 more source

Quantum Algorithms for Quantum Field Theories [PDF]

Science, 2012
Quantum Leap? Quantum computers are expected to be able to solve some of the most difficult problems in mathematics and physics. It is not known, however, whether quantum field theories (QFTs) can be simulated efficiently with a quantum computer.
Jordan, Stephen P., Lee, Keith S. M., Preskill, John   +2 more
openaire   +4 more sources

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, Pierre Le Berre, Hamdi Jaballah, Lukas Fink, Victor Poline, Richard Haettel, Stéphane Grenier, Gilbert Chahine, Ewen Bellec, Guillaume Buatier, Wilfried Hortschitz, Santa Pile, Thomas Schrefl, Nils Blanc, Yuan Hong, Thibaut Devillers, Nora M. Dempsey   +16 more
wiley   +1 more source

Non-orientable AdS3 and its holography

Journal of High Energy Physics
Known solutions to three-dimensional gravity with negative cosmological constant consist of either AdS3 or its orbifolds (or orientifolds). We geometrically derive a novel non-orientable AdS3 spacetime that is an orientifold of a spinor double cover of ...
Shivesh Pathak, Lucas Kocia Kovalsky
doaj   +1 more source

Basic Quantum Algorithms

CoRR, 2022
170 ...
openaire   +2 more sources

Modulating Two‐Photon Absorption in a Pyrene‐Based MOF Series: An In‐Depth Investigation of Structure–Property Relationships

Advanced Functional Materials, EarlyView.
This study investigates H4TBAPy‐based metal–organic frameworks (MOFs) ‐ NU‐1000, NU‐901, SrTBAPy, and BaTBAPy ‐ for multiphoton absorption (MPA) performance. It observes topology‐dependent variations in the 2PA cross‐section, with BaTBAPy exhibiting the highest activity.
Simon N. Deger, Helmy Pacheco Hernandez, Yang Cui, Hongxing Hao, Vanessa Ramm, David C. Mayer, Khai‐Nghi Truong, Roland A. Fischer, Jürgen Hauer, Mariana Kozlowska, Alexander Pöthig   +10 more
wiley   +1 more source

Explainable representation learning of small quantum states

Machine Learning: Science and Technology
Unsupervised machine learning models build an internal representation of their training data without the need for explicit human guidance or feature engineering.
Felix Frohnert, Evert van Nieuwenburg
doaj   +1 more source

A novel quantum algorithm for ant colony optimisation

IET Quantum Communication, 2022
Ant colony optimisation (ACO) is a commonly used meta‐heuristic to solve complex combinatorial optimisation problems like the travelling salesman problem (TSP), vehicle routing problem (VRP) etc.
Mrityunjay Ghosh, Nivedita Dey, Debdeep Mitra, Amlan Chakrabarti   +3 more
doaj   +1 more source

Quantum policy gradient algorithms

CoRR, 2022
Understanding the power and limitations of quantum access to data in machine learning tasks is primordial to assess the potential of quantum computing in artificial intelligence. Previous works have already shown that speed-ups in learning are possible when given quantum access to reinforcement learning environments.
Sofiène Jerbi, Arjan Cornelissen, Maris Ozols, Vedran Dunjko   +3 more
openaire   +4 more sources