Results 31 to 40 of about 137,793 (228)
Quantum Fair Machine Learning [PDF]
Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 2021 In this paper, we inaugurate the field of quantum fair machine learning. We undertake a comparative analysis of differences and similarities between classical and quantum fair machine learning algorithms, specifying how the unique features of quantum computation alter measures, metrics and remediation strategies when quantum algorithms are subject to ...
openaire +2 more sources
Federated Quantum Machine Learning
Entropy, 2021 Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located.
Samuel Yen-Chi Chen, Shinjae Yoo
openaire +4 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
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
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
core +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 +4 more sources
Machine learning for quantum matter [PDF]
Advances in Physics: X, 2020 34 pages, 4 figures, 290 references.
openaire +3 more sources
Challenges and opportunities in quantum machine learning
Nature Computational Science, 2022 At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and high-energy physics. Nevertheless, challenges remain regarding the trainability of QML models.
Marco Cerezo +4 more
openaire +4 more sources
New trends in quantum machine learning (a) [PDF]
Europhysics Letters, 2020 Abstract Here we will give a perspective on new possible interplays between machine learning and quantum physics, including also practical cases and applications. We will explore the ways in which machine learning could benefit from new quantum technologies and algorithms to find new ways to speed up their computations by breakthroughs ...
Buffoni L., Caruso F.
openaire +3 more sources
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