Training quantum Boltzmann machines with the β-variational quantum eigensolver

Autor: Onno Huijgen, Luuk Coopmans, Peyman Najafi, Marcello Benedetti, Hilbert J Kappen

Publikováno v: Machine Learning: Science and Technology, Vol 5, Iss 2, p 025017 (2024)

The quantum Boltzmann machine (QBM) is a generative machine learning model for both classical data and quantum states. Training the QBM consists of minimizing the relative entropy from the model to the target state. This requires QBM expectation valu

Externí odkaz: https://doaj.org/article/f902d820eea341faa3c3a248f68c0868

Postoperative Delirium After Intracranial Surgery: A Retrospective Cohort Study

Autor: Pablo R. Kappen, Hilbert J. Kappen, Clemens M.F. Dirven, Markus Klimek, Johannes Jeekel, Elrozy R. Andrinopoulou, Robert J. Osse, Arnaud J.P.E. Vincent

Publikováno v: World Neurosurgery, 172, e212-e219. Elsevier Inc.
World Neurosurgery, 172, pp. E212-E219
World Neurosurgery, 172, E212-E219

Background: The clinical relevance of postoperative delirium (POD) in neurosurgery remains unclear and should be investigated because these patients are vulnerable. Hence, we investigated the impact of POD, by means of incidence and health outcomes,

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::090880061e45c61f7bd90e6c07809167
https://pure.eur.nl/en/publications/6b4aa6e5-952e-4381-8470-9c0238cf0f24

Learning quantum models from quantum or classical data

Autor: Hilbert J. Kappen

Publikováno v: Journal of Physics A. Mathematical and Theoretical, 53, 21, pp. 1-25
Journal of Physics A. Mathematical and Theoretical, 53, 1-25

In this paper, we address the problem how to represent a classical data distribution in a quantum system. The proposed method is to learn quantum Hamiltonian that is such that its ground state approximates the given classical distribution. We review

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e4c1ed9920a5fbe0a24b29f7ed6ee54
https://doi.org/10.1088/1751-8121/ab7df6

An atomic Boltzmann machine capable of self-adaption

Autor: Brian, Kiraly, Elze J, Knol, Werner M J, van Weerdenburg, Hilbert J, Kappen, Alexander A, Khajetoorians

Publikováno v: Nature nanotechnology. 16(4)

The quest to implement machine learning algorithms in hardware has focused on combining various materials, each mimicking a computational primitive, to create device functionality. Ultimately, these piecewise approaches limit functionality and effici

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::3265c55023fb21bc36bfd37ab4a312d3
https://pubmed.ncbi.nlm.nih.gov/33526837

Bayesian neural networks with variable selection for prediction of genotypic values

Autor: Pascal Duenk, Cornelis A. Albers, Giel H. H. van Bergen, Yvonne C. J. Wientjes, Piter Bijma, Mario P. L. Calus, Hilbert J. Kappen

Publikováno v: Genetics, selection, evolution : GSE, 52(1)
Genetics, Selection, Evolution, 52, 1, pp. 1-14
Genetics Selection Evolution
Genetics Selection Evolution, BioMed Central, 2020, 52 (1), pp.26. ⟨10.1186/s12711-020-00544-8⟩
Genetics Selection Evolution, Vol 52, Iss 1, Pp 1-14 (2020)
Genetics, Selection, Evolution : GSE
Genetics, Selection, Evolution, 52, 1-14
Genetics, selection, evolution : GSE 52 (2020) 1

Background Estimating the genetic component of a complex phenotype is a complicated problem, mainly because there are many allele effects to estimate from a limited number of phenotypes. In spite of this difficulty, linear methods with variable selec

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d020a5097d30801914a67cc252d96449
https://research.wur.nl/en/publications/bayesian-neural-networks-with-variable-selection-for-prediction-o