Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers...
Quantum machine learning (QML) can complement the growing trend of using learned models for a myriad of classification tasks, from image recognition to natural speech processing. There exists the potential for a quantum advantage due to the intractability of quantum operations on a classical computer....
who is the first author of the paper, developed the new approach. Their method involves using ascanning electron microscope(SEM) to collectelectron backscatter diffraction(EBSD) patterns. Compared to other electron diffraction techniques, such as those intransmission electron microscopy...
(NISQ) devices, means researchers have begun building on the quantum-like nature of advanced NLP interactions (along with quantum machine learning techniques) to map DisCoCat diagrams to quantum circuits.The promise of quantum computers, after all, is practically limitless in terms of the ...
Chinese Physics Letters, Sizhuo Yu, Yuan Gao, Bin-Bin Chen and Wei Li describe how machine-learning techniques can help us understand how quantum spin liquids behave, and thereby support experimentalists in their study of “candidate” materials that may (or may not) be quantum spin liquids. ...
Quantum machine learning is an emerging interdisciplinary research area at the intersection of quantum computing and machine learning. Quantum computers use effects such as quantum coherence and quantum entanglement to process information, which is fundamentally different from classical computers. Quant...
The objective of this study is to present a review of Quantum Machine Learning from the perspective of conventional techniques. Departing from giving a research path from fundamental quantum theory through Quantum Machine Learning algorithms from a computer scientist’s perspective, we discuss a set ...
Some of the Machine learning techniques uses a large set of data, makes specific patterns based on past data, and approximates the real future called Data Mining. It is to be noted that data mining is one approach to Machine Learning. Machine learning supports computers in modeling on the ...
When several quantum dots are combined to scale the device up to a large number of qubits, this tuning process becomes enormously time-consuming because the semiconductor quantum dots are not completely identical and must each be characterized individually. Automation thanks to machine learning Now, ...
The method uses a quantum-classical hybrid simulator, where a `quantum stude... J Bang,J Ryu,S Yoo,... - 《New Journal of Physics》 被引量: 12发表: 2014年 AUTOMATED DETECTION OF PARKINSON'S DISEASE BASED ON HYBRID CNN AND QUANTUM MACHINE LEARNING TECHNIQUES IN MRI IMAGES Parkinson's ...