QUANTUM MACHINE LEARNING RESEARCH GROUP


ABOUT US

The QML (Quantum Machine Learning) Research group of AIML (Australian Insitute for Machine Learning), is based since 2020 at the University of Adelaide, South Australia.


PEOPLE

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Dr Michele Sasdelli

Main contact
(see researcher profile)

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Prof Tat-Jun Chin

Researcher profile

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Dr Dzung Doan

Researcher profile

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Frances Yang

PhD student

ALUMNI

Cameron McLeod (Master's graduate)



RESEARCH

Quantum Robust Fitting

Quantum Robust Fitting

ACCV2020

CVPR2022

Graphs

Benchmarking Quantum Annealers

ICCS2022

QBNN

Quantum Binary Neural Network

DICTA2021

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Quantum Boltzmann Machines for training Perceptrons

arxiv



SELECTED PUBLICATIONS

McLeod, C. R., & Sasdelli, M. (2022). Benchmarking D-Wave Quantum Annealers: Spectral Gap Scaling of Maximum Cardinality Matching Problems. In International Conference on Computational Science (pp. 150-163). Springer, Cham. https://doi.org/10.1007/978-3-031-08760-8_13 ; PDF

Doan, A. D., Sasdelli, M., Suter, D., & Chin, T. J. (2022). A hybrid quantum-classical algorithm for robust fitting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 417-427). PDF

Sasdelli, M., & Chin, T. J. (2021). Quantum annealing formulation for binary neural networks. In 2021 Digital Image Computing: Techniques and Applications (DICTA) (pp. 1-10). IEEE. PDF

Chin, T. J., Suter, D., Ch'ng, S. F., & Quach, J. (2020). Quantum robust fitting. In Proceedings of the Asian Conference on Computer Vision. PDF


HONOURS AND AWARDS

DST Best Contribution to Science Award (DICTA 2021)

Cameron Mcleod: Best Computer Science Presentation Award, Ingenuity 2021.