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International QuantumCamp
"Quantum bits" by Argonne National Laboratory is licensed under CC BY-NC-SA 2.0
Mission
Mission
QuantumCamp connects researchers, practitionairs and students who are interested in quantum computing topics. Participants are introduced to educational content in quantum computing and/or - depending on the particular event - new research on quantum computing models and technologies, in particular to state of the art quantum algorithms and up-to-date software solutions and frameworks. Together we dive into the realm of quantum machine learning, optimization and simulation, focusing on algorithms, software and implementation aspects.
Events
Events
QuantumCamp 2021, online event, June 29, 2021
Learn about up-to-date quantum algorithms and frameworks in quantum optimization and quantum machine learning, using the most recent software frameworks by IBM, Google, Xanadu and Rigetti.
Previous Events:
1st International Quantum Camp, Wałbrzych, Poland, February 18-21, 2020, [more]
"IBM Quantum Experience" by IBM Research is licensed under CC BY-ND 2.0
Research
Research
The research in Quantum Computing and in particular Quantum Optimization and Quantum Machine Learning is an emerging and vivid field which started with early works around 2013, exploring approaches to develop more efficient and powerful methods using quantum technologies. This covers resphaping known classical methods in the quantum world but also the development of completely new ways to encode and process information. QuantumCamp is working together with industrial partners and develops prototypes investigating real world performance.
Foto: Tobias Ritz
Teaching at the University of Applied Sciences Zittau/Görlitz
Teaching at the University of Applied Sciences Zittau/Görlitz
Modern Machine Learning (244800) - Deep Learning, Reinforcement L., etc.
Applied Quantum Computing (244850) - different models of quantum computing applied in modern software frameworks for modern algorithmic approaches
Quantum Machine Learning (244900) - quantum neural networks, quantum support vector machines, clustering, PCA, various supervised techniques
Quantum Optimization (244950) - quantum annealing, QAOA, etc.
Literature
Literature
[1] Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017). Quantum machine learning. Nature, 549(7671), 195.
[2] Schuld, M., Sinayskiy, I., & Petruccione, F. (2015). An introduction to quantum machine learning. Contemporary Physics, 56(2), 172-185.
[3] Lloyd, S., Mohseni, M., & Rebentrost, P. (2013). Quantum algorithms for supervised and unsupervised machine learning. arXiv preprint arXiv:1307.0411.
[4] Wittek, P. (2014). Quantum machine learning: what quantum computing means to data mining. Academic Press.
[5] Rebentrost, P., Mohseni, M., & Lloyd, S. (2014). Quantum support vector machine for big data classification. Physical review letters, 113(13), 130503.
[6] Cai, X. D., Wu, D., Su, Z. E., Chen, M. C., Wang, X. L., Li, L., ... & Pan, J. W. (2015). Entanglement-based machine learning on a quantum computer. Physical review letters, 114(11), 110504.
[7] Sheng, Y. B., & Zhou, L. (2017). Distributed secure quantum machine learning. Science Bulletin, 62(14), 1025-1029.
Contact
Contact
Prof. Dr. Jörg Lässig
Head Enterprise Application Development
Hochschule Zittau/Görlitz - University of Applied Sciences, Building GII, Rm. 108, Brückenstraße 1, 02826 Görlitz, Germany
QuantumCamp Office:
Next Camp: Summer 2021, June 29
Contact@QuantumCamp.Org
Hochschule Zittau/Görlitz - University of Applied Sciences - QuantumCamp
Obermarkt 17, 02826 Görlitz, Germany
Join us
Join us
If you are interested to join the next QuantumCamp, please write an email to Prof. Dr. Jörg Lässig, including also the topics of your focus.
The worthwhile problems are the ones you can really solve or help solve, the ones you can really contribute something to. … No problem is too small or too trivial if we can really do something about it.
-Richard Feynman
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