Results 71 to 80 of about 137,793 (228)
A quantum-inspired classifier for clonogenic assay evaluations
Scientific Reports, 2021 Recent advances in Quantum Machine Learning (QML) have provided benefits to several computational processes, drastically reducing the time complexity.
Giuseppe Sergioli +7 more
doaj +1 more source
Advanced Functional Materials, EarlyView.Molten KNO2 treatment induces a lowrefractive index disordered carbon shell on fluorescent nanodiamonds, enhancing fluorescence emission while preserving spin coherence. Machine learningassisted correlative TEMPL enables direct single‐particle resolution of this enhancement relative to air‐oxidized nanodiamonds of similar morphology, establishing a ...
Parkarsh Kumar +10 more
wiley +1 more source
Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models
, 2017 Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions.
Benedetti, Marcello +3 more
core +2 more sources
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
A Future with Quantum Machine Learning
Computer, 2018 Could combining quantum computing and machine learning with Moore’s law produce a true “rebooted computer”? This article posits that a three-technology hybrid-computing approach might yield sufficiently improved answers to a broad class of problems such that energy efficiency will no longer be the dominant concern.
openaire +2 more sources
Atomic Layer Deposition in Transistors and Monolithic 3D Integration
Advanced Functional Materials, EarlyView.Transistors are fundamental building blocks of modern electronics. This review summarizes recent progress in atomic layer deposition (ALD) for the synthesis of two‐dimensional (2D) metal oxides and transition‐metal dichalcogenides (TMDCs), with particular emphasis on their enabling role in monolithic three‐dimensional (M3D) integration for next ...
Yue Liu +5 more
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
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
Advanced Functional Materials, EarlyView.Flexible piezoresistive pressure sensors underpin wearable and soft electronics. This review links sensing physics, including contact resistance modulation, quantum tunneling and percolation, to unified materials/structure design. We highlight composite and graded architectures, interfacial/porous engineering, and microstructured 3D conductive networks
Feng Luo +2 more
wiley +1 more source
QFlow lite dataset: A machine-learning approach to the charge states in quantum dot experiments
, 2018 Over the past decade, machine learning techniques have revolutionized how research is done, from designing new materials and predicting their properties to assisting drug discovery to advancing cybersecurity.
Kalantre, Sandesh S. +4 more
core +2 more sources