Results 71 to 80 of about 154,173 (259)
QDataSet, quantum datasets for machine learning
Scientific Data, 2022 AbstractThe availability of large-scale datasets on which to train, benchmark and test algorithms has been central to the rapid development of machine learning as a discipline. Despite considerable advancements, the field of quantum machine learning has thus far lacked a set of comprehensive large-scale datasets upon which to benchmark the development ...
Elija Perrier +2 more
openaire +4 more sources
Integrative Approaches for DNA Sequence‐Controlled Functional Materials
Advanced Functional Materials, EarlyView.DNA is emerging as a programmable building block for functional materials with applications in biomimicry, biochemical, and mechanical information processing. The integration of simulations, experiments, and machine learning is explored as a means to bridge DNA sequences with macroscopic material properties, highlighting current advances and providing ...
Aaron Gadzekpo +4 more
wiley +1 more source
Clinical data classification with noisy intermediate scale quantum computers
Scientific Reports, 2022 Quantum machine learning has experienced significant progress in both software and hardware development in the recent years and has emerged as an applicable area of near-term quantum computers.
S. Moradi +7 more
doaj +1 more source
Nonnegative/binary matrix factorization with a D-Wave quantum annealer
, 2017 D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest, but have been used for few real-world computations. Machine learning has been identified as an area where quantum annealing may be useful. Here,
Alexandrov, Boian S. +3 more
core +2 more sources
, 2008 We propose a novel notion of a quantum learning machine for automatically controlling quantum coherence and for developing quantum algorithms. A quantum learning machine can be trained to learn a certain task with no a priori knowledge on its algorithm.
Bang, Jeongho +3 more
openaire +2 more sources
Electron–Matter Interactions During Electron Beam Nanopatterning
Advanced Functional Materials, EarlyView.This article reviews the electron–matter interactions important to nanopatterning with electron beam lithography (EBL). Electron–matter interactions, including secondary electron generation routes, polymer radiolysis, and electron beam induced charging, are discussed.
Camila Faccini de Lima +2 more
wiley +1 more source
Advanced Functional Materials, EarlyView.Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
wiley +1 more source
On a quantum inspired approach to train machine learning models
Applied AI Letters, 2023 In this work, a novel technique to train machine learning models is introduced, which is based on digital simulations of certain types of quantum systems.
Jean Michel Sellier
doaj +1 more source
Transforming Bell's Inequalities into State Classifiers with Machine Learning
, 2017 Quantum information science has profoundly changed the ways we understand, store, and process information. A major challenge in this field is to look for an efficient means for classifying quantum state. For instance, one may want to determine if a given
Ma, Yue-Chi, Yung, Man-Hong
core +2 more sources
Bio‐Inspired Molecular Events in Poly(Ionic Liquids)
Advanced Functional Materials, EarlyView.Originating from dipolar and polar inter‐ and intra‐chain interactions of the building blocks, the topologies and morphologies of poly(ionic liquids) (PIL) govern their nano‐ and micro‐processibility. Modulating the interactions of cation‐anion pairs with aliphatic dipolar components enables the tunability of properties, facilitated by “bottom‐up ...
Jiahui Liu, Marek W. Urban
wiley +1 more source