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Distributed Quantum Machine Learning

, 2022
Quantum computers can solve specific complex tasks for which no reasonable-time classical algorithm is known. Quantum computers do however also offer inherent security of data, as measurements destroy quantum states. Using shared entangled states, multiple parties can collaborate and securely compute quantum algorithms.
Niels M. P. Neumann, Robert S. Wezeman
openaire   +3 more sources

Quantum Machine Learning—An Overview

, 2023
Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems.
Kyriaki A. Tychola, George A. Papakostas, Theofanis Kalampokas   +2 more
core   +1 more source

Machine learning for quantum physics and quantum physics for machine learning

, 2023
Research at the intersection of machine learning (ML) and quantum physics is a recent growing field due to the enormous expectations and the success of both fields. ML is arguably one of the most promising technologies that has and will continue to disrupt many aspects of our lives.
openaire   +4 more sources

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, Guglielmo Mazzola, Francesco Tacchino, Olga Dmitriyeva, Tai Bui, Shanshan Huang, Ivano Tavernelli   +6 more
doaj   +1 more source

Quantum machine learning of large datasets using randomized measurements [PDF]

, 2022
Quantum computers promise to enhance machine learning for practical applications. Quantum machine learning for real-world data has to handle extensive amounts of high-dimensional data.
M S Kim, Haug, Tobias, Tobias Haug, Chris N Self, Self, Chris N, Kim, MS, Kim, M. S., Self, Chris N.   +7 more
core   +1 more source

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, Guillaume Verdon, Hsin-Yuan Huang, Lukasz Cincio, Patrick J. Coles   +4 more
openaire   +5 more sources

Machine learning applied to programming quantum computers

, 2019
Click on the DOI link to access the article (may not be free).We apply machine learning to “program” quantum computers, both in simulation and in experimental hardware.
Steck, James E., Thompson, Nathan L., Behrman, Elizabeth C.   +2 more
core   +1 more source

Quantum Gaussian Process Regression for Bayesian Optimization [PDF]

, 2023
Gaussian process regression is a well-established Bayesian machine learning method. We propose a new approach to Gaussian process regression using quantum kernels based on parameterized quantum circuits.
Rapp, Frederic, Roth, Marco
core   +1 more source

New insights into supradense matter from dissecting scaled stellar structure equations

Frontiers in Astronomy and Space Sciences
The 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