Results 51 to 60 of about 8,042,432 (193)

Online Quantum Mixture Regression for Trajectory Learning by Demonstration [PDF]

, 2013
16/01/14 MEB. Pre-print version OK to add.In this work, we present the online Quantum Mixture Model (oQMM), which combines the merits of quantum mechanics and stochastic optimization. More specifically it allows for quantum effects on the mixture states,
Dimitrios Korkinof, Demiris, Y, Korkinof, D, Yiannis Demiris   +3 more
core   +1 more source

Quantum dynamics of machine learning

Acta Physica Sinica
In order to solve the current lack of rigorous theoretical models in the machine learning process, in this paper the iterative motion process of machine learning is modeled by using quantum dynamic method based on the principles of first-principles thinking.
Peng Wang, Maimaitiniyazi Maimaitiabudula   +1 more
openaire   +3 more sources

Advances in quantum machine learning

, 2015
Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting directions for further research. We include both algorithms and experimental implementations in the discussion.
Adcock, Jeremy, Allen, Euan, Day, Matt, Frick, Stefan, Hinchliff, Janna J, Johnson, Mack, Morley-Short, Sam, Pallister, Sam, Price, Alasdair, Stanisic, Stasja   +9 more
openaire   +3 more sources

YAQQ: yet another quantum quantizer design space exploration of quantum gate sets using novelty search

New Journal of Physics
The standard model of quantum computation is based on quantum circuits, where the number and quality of the quantum gates composing the circuit influence the runtime and fidelity of the computation. The fidelity of the decomposition of quantum algorithms,
Aritra Sarkar, Akash Kundu, Matthew Steinberg, Sibasish Mishra, Sebastiaan Fauquenot, Tamal Acharya, Jarosław A Miszczak, Sebastian Feld   +7 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, Kenny Choo, Damian Hofmann, James E.T. Smith, Tom Westerhout, Fabien Alet, Emily J. Davis, Stavros Efthymiou, Ivan Glasser, Sheng-Hsuan Lin, Marta Mauri, Guglielmo Mazzola, Christian B. Mendl, Evert van Nieuwenburg, Ossian O’Reilly, Hugo Théveniaut, Giacomo Torlai, Filippo Vicentini, Alexander Wietek   +18 more
doaj   +1 more source

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, Xi Cao, Weiting Wang, Ziyue Hua, Weizhou Cai, Xuegang Li, Haiyan Wang, Jiaqi Hu, Yipu Song, Dong-Ling Deng, Chang-Ling Zou, Re-Bing Wu, Luyan Sun   +12 more
doaj   +1 more source

Quantum Based Pseudo-Labelling for Hyperspectral Imagery: A Simple and Efficient Semi-Supervised Learning Method for Machine Learning Classifiers

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, Aiswarya Unni, Weiping Zeng   +2 more
doaj   +1 more source

Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning

2025 IEEE International Symposium on Circuits and Systems (ISCAS)
In ...
Jun Qi 0002, Chao-Han Huck Yang, Samuel Yen-Chi Chen, Pin-Yu Chen   +3 more
openaire   +3 more sources

Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches

Computers, 2021
Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods.
Frank Phillipson, Robert S. Wezeman, Irina Chiscop   +2 more
doaj   +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, Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, Hsi-Sheng Goan   +5 more
doaj   +1 more source