Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Elizabeth C. Behrman"'
Publikováno v:
QCE
Bayesian Networks (BN) are probabilistic graphical models that are widely used for uncertainty modeling, stochastic prediction and probabilistic inference. A Quantum Bayesian Network (QBN) is a quantum version of the Bayesian network that utilizes th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b1d8bf53f3172b11e28a8f2dcd25b73
https://doi.org/10.1109/qce49297.2020.00053
https://doi.org/10.1109/qce49297.2020.00053
Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often describe quantum neural network models. In this article, a novel self-supervised s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::013b5b9c84c2c190c1f7433cfd339def
http://arxiv.org/abs/2009.06767
http://arxiv.org/abs/2009.06767
Publikováno v:
Quantum Machine Intelligence. 2
Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quant
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f0b5409e1a9f6e6f17ea8bd15fe6a085
https://doi.org/10.1007/s42484-020-00013-x
https://doi.org/10.1007/s42484-020-00013-x
Publikováno v:
QCE
Major obstacles remain to the implementation of macroscopic quantum computing: hardware problems of noise, decoherence, and scaling; software problems of error correction; and, most important, algorithm construction. Finding truly quantum algorithms
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8be8704ff363ec648ff19afb780336f2
Publikováno v:
Expert Systems with Applications. 176:114768
Probabilistic graphical models such as Bayesian networks are widely used to model stochastic systems to perform various types of analysis such as probabilistic prediction, risk analysis, and system health monitoring, which can become computationally
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2ee1de3c883356c66bfc787b7e7d9283
https://doi.org/10.1016/j.eswa.2021.114768
https://doi.org/10.1016/j.eswa.2021.114768
Publikováno v:
Quantum Information and Computation. 17:460-487
We propose and develop a new procedure, whereby a quantum system can learn to anneal to a desired ground state. We demonstrate successful learning to produce an entangled state for a two-qubit system, then demonstrate generalizability to larger syste
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cde2b5c27e3f993263e7db836f437aaa
https://doi.org/10.26421/qic17.5-6-6
https://doi.org/10.26421/qic17.5-6-6
Publikováno v:
IEEE transactions on neural networks and learning systems. 31(7)
The power of quantum computers is still somewhat speculative. While they are certainly faster than classical ones at some tasks, the class of problems they can efficiently solve has not been mapped definitively onto known classical complexity theory.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e18008bac8aad5d24180658f400ace2
https://pubmed.ncbi.nlm.nih.gov/31484135
https://pubmed.ncbi.nlm.nih.gov/31484135
Publikováno v:
AIAA Scitech 2019 Forum.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3026badc6b15a18d59b08ffbf01efa06
https://doi.org/10.2514/6.2019-0956
https://doi.org/10.2514/6.2019-0956
Designing and implementing algorithms for medium and large scale quantum computers is not easy. In previous work we have suggested, and developed, the idea of using machine learning techniques to train a quantum system such that the desired process i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::892e8997c294206eea30e30a16c22866