Results 61 to 70 of about 154,173 (259)
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
Enhanced Quantum Synchronization via Quantum Machine Learning
, 2019 We study the quantum synchronization between a pair of two-level systems inside two coupled cavities. By using a digital-analog decomposition of the master equation that rules the system dynamics, we show that this approach leads to quantum ...
Cárdenas-López, F. A. +3 more
core +1 more source
Nonunitary quantum machine learning
Physical Review AppliedWe introduce several probabilistic quantum algorithms that overcome the normal unitary restrictions in quantum machine learning by leveraging the linear combination of unitaries (LCU) method. We cover three distinct topics, beginning with quantum native implementations of residual networks (ResNets).
Jamie Heredge +3 more
openaire +2 more sources
Advanced Functional Materials, EarlyView.Molecular engineering of a nonconjugated radical polymer enables a significant enhancement of the glass transition temperature. The amorphous nature and tunability of the polymer, arising from its nonconjugated backbone, facilitates the fabrication of organic memristive devices with an exceptionally high yield (>95%), as well as substantial ...
Daeun Kim +14 more
wiley +1 more source
Two‐Dimensional Materials as a Multiproperty Sensing Platform
Advanced Functional Materials, EarlyView.Various sensing modalities enabled and/or enhanced by two‐dimensional (2D) materials are reviewed. The domains considered for sensing include: 1) optoelectronics, 2) quantum defects, 3) scanning probe microscopy, 4) nanomechanics, and 5) bio‐ and chemosensing.
Dipankar Jana +11 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
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
Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics
Advanced Functional Materials, EarlyView.Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha +18 more
wiley +1 more source
Supervised learning with quantum enhanced feature spaces
, 2018 Machine learning and quantum computing are two technologies each with the potential for altering how computation is performed to address previously untenable problems.
Chow, Jerry M. +6 more
core +1 more source
, 2016 [EN] In a world in which accessible information grows exponentially, the selection of the appropriate information turns out to be an extremely relevant problem. In this context, the idea of Machine Learning (ML), a subfield of Artificial Intelligence, emerged to face problems in data mining, pattern recognition, automatic prediction, among others ...
Maria Schuld, Francesco Petruccione
openaire +3 more sources