Showing 20 of 561 academic articles
Marcello Caleffi, Michele Amoretti, Davide Ferrari, Jessica Illiano et al. • Computer Networks
(2024)
Nowadays, quantum computing has reached the engineering phase, with fully-functional quantum processors integrating hundreds of noisy qubits. Yet – to fully unveil the potential of quantum computing out of the labs into the business reality...
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DOI: https://doi.org/10.1016/j.comnet.2024.110672
Colin P. Williams • Lecture notes in computer science
(1999)
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DOI: https://doi.org/10.1007/3-540-49208-9
Young‐Seok Kim, Andrew Eddins, Sajant Anand, Ken Xuan Wei et al. • Nature
(2023)
Abstract Quantum computing promises to offer substantial speed-ups over its classical counterpart for certain problems. However, the greatest impediment to realizing its full potential is noise that is inherent to these systems. The widely ...
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DOI: https://doi.org/10.1038/s41586-023-06096-3
PMC: 10266970
Yudong Cao, Jonathan Romero, Alán Aspuru‐Guzik • IBM Journal of Research and Development
(2018)
Quantum computing has rapidly advanced in recent years due to substantial development in both hardware and algorithms. These advances are carrying quantum computers closer to their impending commercial utility. Drug discovery is a promising...
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DOI: https://doi.org/10.1147/jrd.2018.2888987
James C. L. Chow • Medical Sciences
(2024)
Quantum computing (QC) represents a paradigm shift in computational power, offering unique capabilities for addressing complex problems that are infeasible for classical computers. This review paper provides a detailed account of the curren...
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DOI: https://doi.org/10.3390/medsci12040067
PMC: 11586987
Jack D. Hidary • Springer eBooks
(2019)
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DOI: https://doi.org/10.1007/978-3-030-23922-0
John P. Preskill • Quantum
(2018)
Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today's classical digital computers, but noise in q...
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DOI: https://doi.org/10.22331/q-2018-08-06-79
Mark Fingerhuth, Tomáš Babej, Péter Wittek • PLoS ONE
(2018)
Open source software is becoming crucial in the design and testing of quantum algorithms. Many of the tools are backed by major commercial vendors with the goal to make it easier to develop quantum software: this mirrors how well-funded ope...
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DOI: https://doi.org/10.1371/journal.pone.0208561
PMC: 6301779
Dylan Herman, Cody Googin, Xiaoyuan Liu, Alexey Galda et al. • RePEc: Research Papers in Economics
(2022)
Quantum computers are expected to surpass the computational capabilities of classical computers during this decade and have transformative impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the fi...
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Open Access
DOI: https://doi.org/10.48550/arxiv.2201.02773
Giulio Coluccia, Chiara Ravazzi, Enrico Magli • Springer briefs in electrical and computer engineering
(2015)
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DOI: https://doi.org/10.1007/978-981-287-390-3
Wen-Yen Chen, Yangqiu Song, Hongjie Bai, Chih‐Jen Lin et al. • IEEE Transactions on Pattern Analysis and Machine Intelligence
(2010)
Spectral clustering algorithms have been shown to be more effective in finding clusters than some traditional algorithms, such as k-means. However, spectral clustering suffers from a scalability problem in both memory use and computational ...
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DOI: https://doi.org/10.1109/tpami.2010.88
Bharadwaj Veeravalli, Debasish Ghose, Thomas G. Robertazzi • Cluster Computing
(2003)
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DOI: https://doi.org/10.1023/a:1020958815308
Kemal Efe • Computer
(1982)
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DOI: https://doi.org/10.1109/mc.1982.1654050
Michael C. Loui • Information and Control
(1984)
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Open Access
DOI: https://doi.org/10.1016/s0019-9958(84)80022-4
Luiz F. Bittencourt, Alfredo Goldman, Edmundo R. M. Madeira, Nelson L. S. da Fonseca et al. • Computer Science Review
(2018)
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Open Access
DOI: https://doi.org/10.1016/j.cosrev.2018.08.002
Michael P. Polis, Raymond E. Goodson • Proceedings of the IEEE
(1976)
This paper surveys the distributed system parameter identification problem and concludes that it can be treated as a series of largely independent subproblems, each of which must be considered in any attempt to treat the problem in applicat...
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DOI: https://doi.org/10.1109/proc.1976.10066
Mark A. Paskin, Carlos Guestrin • arXiv (Cornell University)
(2012)
Probabilistic inference problems arise naturally in distributed systems such as sensor networks and teams of mobile robots. Inference algorithms that use message passing are a natural fit for distributed systems, but they must be robust to ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1207.4174
Mohammad Alauthman, Ammar Almomani, Ahmad Al–Qerem, Mohammad Ali Al Khaldy et al. • Advances in information security, privacy, and ethics book series
(2024)
This chapter explores the impact of quantum computing on cybersecurity, comparing classical and quantum techniques. It provides an overview of the current quantum landscape, focusing on the threats posed by quantum algorithms to traditional...
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DOI: https://doi.org/10.4018/979-8-3693-5330-1.ch004
Vivi Năstase, Rada F. Mihalcea, Dragomir Radev • Natural Language Engineering
(2015)
Abstract Graphs are a powerful representation formalism that can be applied to a variety of aspects related to language processing. We provide an overview of how Natural Language Processing problems have been projected into the graph framew...
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Open Access
DOI: https://doi.org/10.1017/s1351324915000340
Fredrik Olsson • KTH Publication Database DiVA (KTH Royal Institute of Technology)
(2009)
Active learning is a supervised machine learning technique in which the learner is in control of the data used for learning. That control is utilized by the learner to ask an oracle, typically a human with extensive knowledge of the domain ...
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