Results 61 to 70 of about 137,793 (228)
Quantum ensembles of quantum classifiers
, 2017 Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis.
Petruccione, Francesco, Schuld, Maria
core +2 more sources
Verifying Fairness in Quantum Machine Learning
, 2022 AbstractDue to the beyond-classical capability of quantum computing, quantum machine learning is applied independently or embedded in classical models for decision making, especially in the field of finance. Fairness and other ethical issues are often one of the main concerns in decision making.
Ji Guan 0001 +2 more
openaire +3 more sources
Advanced Functional Materials, EarlyView.This review systematically highlights the latest achievements in mixed‐valence states relevant to hydrogen and oxygen evolution reactions, providing essential insights into future directions and methods for large‐scale practical implementation. This critical review is expected to provide an overview of recent advancements in diverse valence‐state metal
Jitendra N. Tiwari +4 more
wiley +1 more source
Machine Learning Bell Nonlocality in Quantum Many-body Systems
, 2017 Machine learning, the core of artificial intelligence and big data science, is one of today's most rapidly growing interdisciplinary fields. Recently, its tools and techniques have been adopted to tackle intricate quantum many-body problems. In this work,
Deng, Dong-Ling
core +1 more source
Slight Truncation Changes in Iron Oxide Nanocubes Strongly Affect Their Magnetic Properties
Advanced Functional Materials, EarlyView.Subtle variations in nanoparticle morphology can lead to significant changes in functional properties. An automated shape‐fitting method captures minor differences in corner truncation between iron oxide nanocubes of similar sizes synthesized under identical conditions, revealing pronounced disparities in their magnetic and hyperthermia behavior ...
Kingsley Poon +7 more
wiley +1 more source
On Quantum Methods for Machine Learning Problems Part II: Quantum Classification Algorithms
Big Data Mining and Analytics, 2020 This is a review of quantum methods for machine learning problems that consists of two parts. The first part, "quantum tools", presented some of the fundamentals and introduced several quantum tools based on known quantum search algorithms.
Farid Ablayev +5 more
doaj +1 more source
Machine learns quantum complexity
Journal of the Korean Physical Society18 pages, 8 figures, references ...
Bak, Dongsu +3 more
openaire +2 more sources
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
Advanced Functional Materials, EarlyView.Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner +14 more
wiley +1 more source
Machine Learning and Knowledge ExtractionQuantum computing, with its foundational principles of superposition and entanglement, has the potential to provide significant quantum advantages, addressing challenges that classical computing may struggle to overcome.
Maria Revythi, Georgia Koukiou
doaj +1 more source