Annotation

The workshop will consist of 5 talks. The tutor’s names, annotations, and titles are specified below.

Benefits for attendees, what will they learn

Attendees will gain valuable insights into the field of Quantum Machine Learning through a series of engaging talks. They will explore how Quantum Convolutional Neural Networks can classify quantum states, understand the application of QML in face recognition technology, and learn about the automation of QML procedures.

Agenda

Talk Title: Guaranteeing accurate Quantum Phase Classification with Quantum Convolutional Neural Networks

Speaker: Nathan McMahon

Abstract: Quantum convolutional neural networks (QCNNs) have been proposed as a method to classify quantum state inputs into their different phases of matter. This is in contrast to traditional methods based on order parameters, which raises the question about the accuracy of the QCNN phase recognition algorithm.

In this talk I will introduce these QCNNs and provide some intuition regarding how they were designed. Then, by employing the adiabatic state preparation algorithm as a tool, analyse the accuracy of the QCNN for classifying the Hamiltonians of the cluster-Ising model into their respective phases. By studying the distribution of how the QCNN output changes with Hamiltonian parameters, we show properties of phases that imply correct classification. Using these results, we construct an infinite family of QCNNs with similar classification abilities.

About the speaker: Nathan McMahon obtained his PhD in 2018 in quantum information theory from the University of Queensland, Australia. Since then, he has held postdoctoral positions at the University of Queensland, Friedrich-Alexander University Erlangen-Nurnberg, and most recently, Leiden University. He has been awarded an Alexander von Humboldt fellowship and is currently interested in fundamental aspects of quantum machine learning.

Talk Title: Face Gender Recognition Using Quantum Machine Learning on NVIDIA CUDA Quantum Simulator

Speaker: Jiří Tomčala

Abstract: Quantum machine learning (QML) is a research area that combines algorithms from quantum computing and machine learning into one functional system. This poster presents the results of a concrete example of its successful use to recognize people's gender by their faces. This recognition was performed using a learned hybrid quantum neural network that contains both neural layers implemented by a classical computer and neural layers implemented by a quantum computer in which a variational quantum circuit is programmed. The most computationally demanding part was teaching this network a training set containing several hundred female and male faces. The learning was done relatively quickly thanks to the powerful NVIDIA CUDA Quantum simulator. This platform allows simulations to run on supercomputing nodes with many CPU and GPU cores, significantly speeding them up compared to other quantum frameworks.

About the speaker: Jiří Tomčala is a researcher in the Quantum Computing laboratory of the IT4Innovations National Supercomputer Center. He graduated with a degree in Applied Mathematics in 2016 and earned his Ph.D. in Computer Science in 2021. In the years 2021, 2022, 2023, and 2024, he received the Certificate of Quantum Excellence and Quantum Challenge achievement from IBM Quantum. Also participated as a mentor for quantum computing projects in the PRACE Summer of HPC 2021 and 2022. He is an active publishing researcher in the field of quantum computing and regularly contributes his latest results to scientific conferences and journals. His current research interest lies in using quantum computation to solve optimization problems, factorization, and quantum machine learning, especially in hybrid quantum neural networks. Thus, he focuses on variational quantum algorithms, the composition of variational quantum circuits and the specific applications of Shor's and Grover's algorithms. 

Talk Title: On Quantum Support Vector Machines in the NISQ era

Speaker: Paulina Lewandowska

Abstract: Support vector machines (SVMs) are well-established classifiers that are effectively deployed in an array of classification tasks. In this talk, I present quantum version of SVM and recent progress. We will discuss  its potential advantage for classification problem as well as its weak points. Finally, we will show the performance of QSVM by using IBMQ computers.

About the speaker: Paulina Lewandowska has completed her PhD at the Institute of Theoretical and Applied Informatics, Polish Academy of Science (PAS). At the same time, she was also one of the members of the Quantum Systems of Informatics Group in PAS. There, she has gained knowledge about quantum computing, benchmarking, and validation methods of gate-based quantum computers. Her research also includes quantum causal structure theory, quantum learning, and, recently, quantum game theory.  Recently, she joined the Quantum Computing Lab to deepen her quantum computing experience.

Talk Title: Automated Quantum Machine Learning: Problems, Solutions and Research Directions

Speaker: Tomasz Rybotycki

Abstract: Quantum Machine Learning (QML) is an interdisciplinary field that is developing at an enormous rate. Keeping up with the latest advancements is challenging, even for QML practitioners. Automated Quantum Machine Learning (AQML) aims to address this challenge by automating parts of the machine learning pipeline. Ideally, AQML would enable users to apply QML or hybrid models without needing any expertise in Quantum Machine Learning or Quantum Computing in general.

This talk will focus on AQML solutions. I will begin by discussing common QML problems that can be partially automated. Then, I will provide an overview of AQML solutions designed to tackle these challenges. In particular, I will introduce AQMLator — an automated quantum machine learning platform I co-developed — and highlight its features and limitations. I'll conclude the talk by sharing insights into potential future directions for AQML research and the development of AQMLator.

About the speaker: Dr Tomasz Rybotycki obtained his PhD in 2023 from the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. His doctoral advisor was Prof. Piotr Kulczycki. During his PhD, dr Rybotycki worked on the predictive estimation of the density of non-stationary data streams, where he designed a kernel density estimator-based algorithm for the predictive estimation of data density. The title of his thesis was “Estimation of data density for non-stationary streaming data”. During his PhD, he also obtained a B.Sc. degree in Physics after successfully defending his B.Sc. thesis entitled “Framework for performing experiments on IBM Quantum Computers”.

Talk Title: The many faces of quantum computing

Speaker: Piotr Gawron

Abstract: The gate model of quantum computing is the most intuitive one since it resembles the classical model of computation based on logic gates. Yet, quantum computing can be performed using a variety of models, such as measurement-based quantum computing, reservoir computing, quantum walks, quantum adiabatic and annealing computing, optical quantum computing, and others. During the talk, we will present the multitude of ideas that the quantum computing community has invented in the last decades.

About the speaker: Piotr Gawron is a computer scientist specialising in quantum computing and artificial intelligence methods. He is currently the leader of a research team at Nicolaus Copernicus Astronomical Center of the Polish Academy of Sciences, which is studying the application of quantum machine learning to the processing of Earth observation image data, the application of quantum and classical machine learning techniques to gravitational waves and dark matter detection. He also leads a team at AGH Center of Excellence for Artificial Intelligence, where he focuses on quantum data fusion as part of the ARTIQ project. He is a visiting professor at the European Space Agency in the Phi-lab@ESRIN laboratory in Italy.