# Quantum

Machine Learning Conference 2024

Date: 16.03.2024

Form: Online event

## Agenda (times in UTC+1)

11:00: "Introduction to Quantum Machine Learning", Paweł Gora

11:30: "Machine learning and optical quantum information", Karol Bartkiewicz

12:00: "Detecting clouds in multispectral satellite images using quantum-kernel support vector machines", Grzegorz Czelusta

12:30: "Introduction to Variational Quantum Algorithms", Sebastian Zając

## Speakers

Paweł Gora is the Founder and CEO of the Quantum AI Foundation aiming to support education, research, and collaboration in new technologies (especially quantum computing and AI). He is also the Chairperson of the Board of QWorld. More info: https://www.mimuw.edu.pl/~pawelg.

"Introduction to Quantum Machine Learning"

Abstract: In this talk, I will give an introduction to quantum machine learning. First, I will explain what quantum computing is and how it is different from classical computing paradigm. Later, I will tell how machine learning could be enhanced using quantum computing, presenting some of the possible opportunities and existing challenges. Finally, I will present how machine learning could be applied in the quantum computing domain, and outline how one can continue education and start a professional career in the quantum machine learning domain.

K. Bartkiewicz earned PhD in physics in 2012 at AMU in Poznan, Poland. As of 2019, he is an associate professor at the Faculty of Physics at AMU. For ten years he has been working as a researcher (applied physics) at Palacký University in the Czech Republic. He has co-authored more than 50 publications on quantum optics, quantum information processing and quantum information, two of which on secure quantum communication protocols have been commented on in specialized (e.g. Nature Physics) and popular media (e.g. New Scientist, Science Daily, TVN 24). For the past six years, he has been working on quantum machine learning as one of the most promising applications of quantum computing, in collaboration, e.g., with RIKEN Center for Quantum Computing (Japan).

"Machine learning and optical quantum information"

Abstract: Classical programming means writing explicit instructions so that a program processes the input data and correctly answers our questions. Machine learning (ML) is a branch of artificial intelligence research that uses implicit programming, where the program does not receive explicit instructions. This method is particularly suitable for problems that are intuitive to humans but difficult to convert to a set of machine instructions. Some complex problems resist known ML methods, especially in quantum systems [1,3]. E.g. designing new drug molecules or supervising quantum communication networks, which under certain assumptions should be protected from eavesdropping by the laws of quantum physics. These tasks quickly become unfeasible as the complexity of the problem increases. Solutions to such problems must be sought using quantum computing for ML [1,2]. This is the original motivation to combine ML and quantum physics [1]. However, there are many other reasons to do so. In particular, ML can be used to motivate theoretical and experimental research in quantum information, quantum state engineering, classification, and detection. To illustrate this, I will discuss a few assorted examples of combining ML and quantum information processing, including [2,4,5,6,7].

References

[1] J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, S. Lloyd, Nature 549, 195 (2017).

[2] V. Trávníček, K. Bartkiewicz, A. Černoch, K. Lemr, Phys. Rev. Lett. 123, 260501 (2019).

[3] G. Carleo et al., Rev. Mod. Phys. 91, 045002 (2019).

[4] K. Bartkiewicz, C. Gneiting, A. Černoch, K. Jiráková, K. Lemr, F. Nori, Sci. Rep. 10, 12356 (2020).

[5] J. Jašek, K. Jiráková, K. Bartkiewicz, A. Černoch, T. Fürst, K. Lemr, Opt. Express 27, 32454 (2019).

[6] K. Bartkiewicz, P. Tulewicz, J. Roik, K. Lemr, Sci. Rep. 13, 12893 (2023).

[7] J. Roik, K. Bartkiewicz, A. Černoch et al. Quantum Inf. Process.23, 89 (2024).

Grzegorz Czelusta is a doctoral candidate at the Faculty of Physics, Astronomy, and Applied Computer Science at Jagiellonian University in Krakow. He earned his master's degree from the same department, with a thesis on a quantum gravity model of Causal Dynamical Triangulations. Grzegorz's current research involves using quantum algorithms to simulate physics phenomena at the Planck scale. He is investigating the relationship between the quantum structure of space-time and quantum information. Besides his PhD research, he is also participating in projects related to quantum machine learning in satellite data analysis as well as cryptography, both quantum and post-quantum.

"Detecting clouds in multispectral satellite images using quantum-kernel support vector machines"

Abstract: Support vector machines (SVMs) are well-established classifiers that are used effectively in a variety of pattern recognition and classification tasks. In this talk, I consider the extension of classical SVMs with quantum kernels and their application to satellite data analysis. The design and implementation of SVMs with quantum kernels (hybrid SVMs) is presented. It consists of the Quantum Kernel Estimation (QKE) procedure combined with a classical SVM training routine. The pixel data is mapped to Hilbert space using ZZ feature maps acting on the parameterised initial state. The parameters are optimised to maximise the kernel target alignment. We address the problem of cloud detection in satellite image data, which is one of the key steps in the processing chain of both ground-based and on-board satellite image data analysis. Experiments performed on the Landsat-8 multispectral benchmark dataset show that the simulated hybrid SVM successfully classifies satellite images with an accuracy comparable to classical SVMs.

Sebastian Zając is an assistant professor at the Decision Analysis and Support Unit of the SGH Warsaw School of Economics in Poland. He is a theoretical physicist and researcher keen to explore the intersection of cutting-edge technologies and practical business applications. His research focuses on AI methodology in credit risk, MLops practices in a cloud environment, Quantum Machine Learning, and Quantum Computing on NISQ devices. His dedication lies in exploring the potential of quantum computing to solve complex problems with time series and graph data.

"Introduction to Variational Quantum Algorithms"

Abstract: Variational Quantum Algorithms, also known as VQAs, offer a promising approach to harnessing quantum computing’s capabilities, even when limited qubits and noise constraints are present. In this workshop, we will explore the use of parametrized quantum circuits (PQCs) to construct a quantum discriminative model for a binary classification problem and for data encoding. We will build our problem from scratch and use the Qiskit API for quantum neural networks.