Results 41 to 50 of about 154,173 (259)
Design and analysis of quantum machine learning: a survey
Connection ScienceMachine learning has demonstrated tremendous potential in solving real-world problems. However, with the exponential growth of data amount and the increase of model complexity, the processing efficiency of machine learning declines rapidly.
Linshu Chen +6 more
doaj +1 more source
NetKet: A machine learning toolkit for many-body quantum systems
SoftwareX, 2019 We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and flexible implementation of neural-network quantum states, which are used ...
Giuseppe Carleo +18 more
doaj +1 more source
Remote Sensing, 2022 A quantum machine is a human-made device whose collective motion follows the laws of quantum mechanics. Quantum machine learning (QML) is machine learning for quantum computers.
Riyaaz Uddien Shaik +2 more
doaj +1 more source
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
Machine learns quantum complexity
Journal of the Korean Physical Society18 pages, 8 figures, references ...
Bak, Dongsu +3 more
openaire +2 more sources
Synergic quantum generative machine learning
Scientific Reports, 2023 Abstract We introduce a new approach towards generative quantum machine learning significantly reducing the number of hyperparameters and report on a proof-of-principle experiment demonstrating our approach. Our proposal depends on collaboration between the generators and discriminator, thus, we call it quantum synergic generative ...
Karol Bartkiewicz +3 more
openaire +4 more sources
Machine Learning Non-Markovian Quantum Dynamics [PDF]
Physical Review Letters, 2020 Machine learning methods have proved to be useful for the recognition of patterns in statistical data. The measurement outcomes are intrinsically random in quantum physics, however, they do have a pattern when the measurements are performed successively on an open quantum system.
I. A. Luchnikov +3 more
openaire +3 more sources
Quantum Emitters in Hexagonal Boron Nitride: Principles, Engineering and Applications
Advanced Functional Materials, EarlyView.Quantum emitters in hexagonal boron nitride have emerged as a promising candidate for quantum information science. This review examines the fundamentals of these quantum emitters, including their level structures, defect engineering, and their possible chemical structures.
Thi Ngoc Anh Mai +8 more
wiley +1 more source
Variational Quantum Circuits for Deep Reinforcement Learning
IEEE Access, 2020 The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains.
Samuel Yen-Chi Chen +5 more
doaj +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