Results 241 to 250 of about 24,428 (295)

Correction: Vallverdú, J.; Rius, G. NeuroQ: Quantum-Inspired Brain Emulation. <i>Biomimetics</i> 2025, <i>10</i>, 516. [PDF]

Biomimetics (Basel)
Vallverdú J, Rius G.
europepmc   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

Demonstration of measurement-free universal logical quantum computation. [PDF]

Nat Commun
Butt F, Pogorelov I, Freund R, Steiner A, Meyer M, Monz T, Müller M.   +6 more
europepmc   +1 more source

Shadows of quantum error correction: Learning and interpretation

Anonymous
openalex   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Correction to "Dye Degradation, Antimicrobial Activity, and Molecular Docking Analysis of Samarium-Grafted Carbon Nitride Doped-Bismuth Oxobromide Quantum Dots". [PDF]

Glob Chall
europepmc   +1 more source

Security of Quantum Key Distribution with One-Time-Pad-Protected Error Correction and Its Performance Benefits. [PDF]

Entropy (Basel)
Novak R.
europepmc   +1 more source

A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes

Advanced Intelligent Discovery, EarlyView.
Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar, Tiago L. P. Galvão, José D. Gouveia, José R. B. Gomes   +3 more
wiley   +1 more source

Quantum-secured routing in drone communication for 6G-enabled smart mobility. [PDF]

Sci Rep
Hafeez S, Abro GEM, Memon SA, Khan TA, Memon I, Nasir H.   +5 more
europepmc   +1 more source

Application of Neural Networks for Advanced Ir Spectroscopy Characterization of Ceria Catalysts Surfaces

Advanced Intelligent Discovery, EarlyView.
A novel convolutional neural network architecture enables rapid, unsupervised analysis of IR spectroscopic data from DRIFTS and IRRAS. By combining synthetic data generation with parallel convolutional layers and advanced regularization, the model accurately resolves spectral features of adsorbed CO, offering real‐time insights into ceria surface ...
Mehrdad Jalali, Lachlan Caulfield, Eric Sauter, Alexei Nefedov, Chengwu Yang, Christof Wöll   +5 more
wiley   +1 more source