Topics

Quantum Machine Learning

Seminar an der Universität des Saarlandes, Fachrichtung Informatik, LSF 136315

The topics for seminar time slots (see schedule) are as follows. Currently assigned topics are marked in red. Selected background literature is indicated below; topic reference papers are available in the web or on request from seminar organizers.

Topic #	Topic
1	Quantum-Supported Image Analysis Mengoni, R., Incudini, M. & Di Pierro, A. (2021): Facial Expression Recognition on a Quantum Computer. Quantum Machine Intelligence, 3.1:1-11.
2	Quantum Natural Langugage Processing Coecke, B. et al. (2020): Foundations for Near-Term Quantum Natural Language Processing. arXiv preprint arXiv:2012.03755.
3	Quantum Boltzman Machine (I) Amin, MH. et al. (2018): Quantum Boltzmann Machine. Physical Review X 8.2:021050. [Adiabatic Quantum Computation Model]
4a	Quantum Boltzman Machine (II) Shingu, Y. et al. (2021): Boltzmann Machine Learning with a Variational Quantum Algorithm. Physical Review A 104.3:032413. [Gate-Based Quantum Computation Model]
4	Quantum Deep Reinforcement Learning (I) Niraula, D., et al. (2021): Quantum Deep Reinforcement Learning for Clinical Decision Support in Oncology: Application to Adaptive Radiotherapy. Scientific reports, 11(1), 1-13. [Adiabatic Quantum Computation Model]
5	Quantum Deep Reinforcement Learning (II) Chen, S.Y.C. et al. (2020): Variational Quantum Circuits for Deep Reinforcement Learning. IEEE Access, 8:141007-141024. Cf.: Lockwood, O., & Si, M. (2020). Reinforcement Learning with Quantum Variational Circuit. Proc. AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 16(1):245-251
6	Quantum Computing for Optimization (I) Hodson, M. et al. (2019): Portfolio Rebalancing Experiments Using the Quantum Alternating Operator Ansatz. arXiv preprint arXiv:1911.05296 Cf.: Egger, D.J., et al. (2020): Quantum computing for finance: State-of-the-art and future prospects. IEEE Transactions on Quantum Engineering, 1.
7	Quantum Computing for Optimization (II) Dalyac, C. et al. (2021): Qualifying Quantum Approaches for Hard Industrial Optimization Problems. A Case Study in the Field of Smart-Charging of Electric Vehicles. EPJ Quantum Technology, 8(1):12.

Selected background references:

Quantum Computing:

Nielsen, M. & Chuang I. (2002): Quantum Computation and Quantum Information.
Biamonte, J. et al. (2017): Quantum Machine Learning. Nature, 549(7671):195-202.
Bus, J.C.P. (2020): The Quantum Approximate Optimization Algorithm: Performance on Max-Cut using Heuristic Parameter Determination. Bachelor Thesis, TU Delft, The Netherlands
de Falco, D., & Tamascelli, D. (2011): An Introduction to Quantum Annealing. RAIRO-Theoretical Informatics and Applications, 45(1)
Herman, D., et al. (2022): A Survey of Quantum Computing for Finance.

Machine Learning:

Mitchell, T. (1997): Machine Learning. McGraw-Hill
Goodfellow, I.J. et al. (2016): Deep Learning. MIT Press,
Nielsen, M. (2017): Neural Networks and Deep Learning.
http://deeplearning.net