Results 41 to 50 of about 137,793 (228)

Strategy for quantum algorithm design assisted by machine learning

, 2014
We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum-classical hybrid simulator, where a "quantum student" is being taught by a "classical teacher." In other words, in our method, the learning system is
Bang, Jeongho, Lee, Jinhyoung, Pawlowski, Marcin, Ryu, Junghee, Yoo, Seokwon   +4 more
core   +1 more source

Quantum Embedding Search for Quantum Machine Learning

IEEE Access, 2022
This paper introduces a novel quantum embedding search algorithm (QES, pronounced as "quest"), enabling search for optimal quantum embedding design for a specific dataset of interest. First, we establish the connection between the structures of quantum embedding and the representations of directed multi-graphs, enabling a well-defined search space ...
Nam Nguyen 0003, Kwang-Cheng Chen
openaire   +3 more sources

Design and analysis of quantum machine learning: a survey

Connection Science
Machine 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, Tao Li, Yuxiang Chen, Xiaoyan Chen, Marcin Wozniak, Neal Xiong, Wei Liang   +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, 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 for quantum anomaly detection

, 2017
Anomaly detection is used for identifying data that deviate from `normal' data patterns. Its usage on classical data finds diverse applications in many important areas like fraud detection, medical diagnoses, data cleaning and surveillance.
Liu, Nana, Rebentrost, Patrick
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   +2 more sources

Toward Knowledge‐Based Workflows: A Semantic Approach to Atomistic Simulations for Mechanical and Thermodynamic Properties

Advanced Engineering Materials, EarlyView.
Knowledge‐based atomistic workflows are presented for mechanical and thermodynamic properties. By coupling modular simulations with ontology‐aligned metadata and provenance, Fe case studies on elastic behavior, defects, thermal properties, and Hall–Petch strengthening reveal how FAIR, queryable, and reusable simulation data can be generated. Mechanical
Abril Azócar Guzmán, Hoang‐Thien Luu, Sarath Menon, Tilmann Hickel, Nina Merkert, Stefan Sandfeld   +5 more
wiley   +1 more source

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