Quantum Machine Learning group

• Karol Bartkiewicz (Ph.D., hab.),
• Karel Lemr (Ph.D., hab.),
• Antonín Černoch (Ph.D.),
• Vojtěch Trávníček (Ph.D.),
• Kateřina Jiráková (Ph.D.),
• Patrycja Tulewicz (Ph.D. candidate),
• Jan Roik (Ph.D. candidate),
• Group at RIKEN

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Research grants

• 2019-2021 (UPOL): Kernel based quantum machine learning in optical circuits, GACR, grant No. 19-19002S

Our quantum kernels can be used for solving high-dimensional classification problems and could potentially be computed faster than their classical counterparts. Popular problems solved by classification algorithms include image recognition (e.g. face detection or character recognition), speech recognition (e.g. voice user interfaces), medical diagnoses (e.g. associating results of medical tests with a class of diseases), real-time specific data extraction from vast amounts of unstructured data (e.g. classification of patterns in unstructured data) and many more. Classification can also be used as an initial phase for predictive computations that help to make the best decision based on the available data (e.g., managing risk, security, traffic, procurement etc.). We believe that this quantum-enhanced approach is useful especially in cases where it is difficult or impossible to achieve the result on time with classical computing.

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Our machine learning publications

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About our results

• Quantum Afrika 6 (online), September 12-16, 2022

• KCIK, II R.S. Ingarden Memorial Session (online), Wednesday, November 24, 2021