Quantum
Machine Learning Conference 2024

• Date: 16.03.2024

• Form: Online event

• Video recording

Paweł Gora

"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. 

Karol Bartkiewicz

"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

"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

"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.