Results 41 to 50 of about 21,815 (240)
Experimental quantum end-to-end learning on a superconducting processor
npj Quantum Information, 2023 Machine learning can be enhanced by a quantum computer via its inherent quantum parallelism. In the pursuit of quantum advantages for machine learning with noisy intermediate-scale quantum devices, it was proposed that the learning model can be designed ...
Xiaoxuan Pan +12 more
doaj +1 more source
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
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
Electroactive Metal–Organic Frameworks for Electrocatalysis
Advanced Functional Materials, EarlyView.Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska +7 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
Quantum Machine Learning Playground
IEEE Computer Graphics and ApplicationsAccepted to IEEE Computer Graphics and Applications.
Pascal Debus +2 more
openaire +3 more sources
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