Results 31 to 40 of about 154,173 (259)
Extending the reach of quantum computing for materials science with machine learning potentials
AIP Advances, 2022 Solving electronic structure problems represents a promising field of applications for quantum computers. Currently, much effort is spent in devising and optimizing quantum algorithms for near-term quantum processors, with the aim of outperforming ...
Julian Schuhmacher +6 more
doaj +1 more source
Basic protocols in quantum reinforcement learning with superconducting circuits [PDF]
, 2017 Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops. Quantum artificial intelligence and quantum machine learning are emerging fields inside quantum technologies which may enable quantum devices ...
Lamata, Lucas
core +3 more sources
Quantum adiabatic machine learning by zooming into a region of the energy surface [PDF]
, 2020 Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification.
Job, Joshua +5 more
core
Revealing quantum chaos with machine learning
, 2020 Understanding properties of quantum matter is an outstanding challenge in science. In this paper, we demonstrate how machine-learning methods can be successfully applied for the classification of various regimes in single-particle and many-body systems ...
Fedorov, A. K. +4 more
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Machine Learning for Quantum Metrology [PDF]
11th Italian Quantum Information Science conference (IQIS2018), 2019 Phase estimation represents a significant example to test the application of quantum theory for enhanced measurements of unknown physical parameters. Several recipes have been developed, allowing to define strategies to reach the ultimate bounds in the asymptotic limit of a large number of trials. However, in certain applications it is crucial to reach
Spagnolo, Nicolò +5 more
openaire +2 more sources
Experimental demonstration of quantum learning speed-up with classical input data
, 2018 We consider quantum-classical hybrid machine learning in which large-scale input channels remain classical and small-scale working channels process quantum operations conditioned on classical input data. This does not require the conversion of classical (
Bang, Jeongho +6 more
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Implementable Quantum Classifier for Nonlinear Data [PDF]
, 2018 In this Letter, we propose a quantum machine learning scheme for the classification of classical nonlinear data. The main ingredients of our method are variational quantum perceptron (VQP) and a quantum generalization of classical ensemble learning.
Du, Yuxuan +3 more
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New insights into supradense matter from dissecting scaled stellar structure equations
Frontiers in Astronomy and Space SciencesThe strong-field gravity in general relativity (GR) realized in neutron stars (NSs) renders the equation of state (EOS) P(ε) of supradense neutron star matter to be essentially nonlinear and refines the upper bound for ϕ≡P/ε to be much smaller than the ...
Bao-Jun Cai, Bao-An Li
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 +12 more
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Modeling Electronic Quantum Transport with Machine Learning [PDF]
, 2014 We present a Machine Learning approach to solve electronic quantum transport equations of one-dimensional nanostructures. The transmission coefficients of disordered systems were computed to provide training and test datasets to the machine. The system's
Lopez-Bezanilla, Alejandro +1 more
core +2 more sources