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Quantum Machine Learning for Africa
Deep Learning Indaba 2025
Kigali, Rwanda
IMPORTANT: WORKSHOP ATTENDANCE IS ONLY POSSIBLE FOR REGISTERED INDABA ATTENDEES
SAVE THE DATE: 22 August 2025
SAVE THE DATE: 22 August 2025
About
About
This workshop will introduce the foundations of Quantum Machine Learning (QML) to its audience. The 2025 workshop edition will have a focus on healthcare, exploring a cancer classification task using histopathology images. Participants will learn the fundamentals of QML, explore hybrid approaches combining classical methods with quantum computing, and gain hands-on experience with the Qiskit programming SDK. The workshop aims to inspire and equip young African researchers with the skills to apply QML to real-world problems in healthcare, disease research, and beyond, fostering innovation and collaboration in the field.
Keen to showcase your work??
Keen to showcase your work??
Submit your poster abstract here. The top 3 peer-reviewed abstracts will be selected for a lightning talk, and the rest will be invited to present their posters to attendees during the networking session. Abstracts must showcase a link to quantum computing or quantum machine learning but may be a proposal, research progress or summary of own published work.
Submit your poster abstract here. The top 3 peer-reviewed abstracts will be selected for a lightning talk, and the rest will be invited to present their posters to attendees during the networking session. Abstracts must showcase a link to quantum computing or quantum machine learning but may be a proposal, research progress or summary of own published work.
Topic Ideas
Topic Ideas
Quantum Machine Learning
Quantum Data Preprocessing
Quantum Natural Language Processing
Quantum Game Theory
Quantum Algorithms
Quantum Optimization
AI for Quantum Computing and QML
Quantum Error Mitigation and Correction
Quantum Applications (Healthcare, Finance, IoT...)
Important dates:
Important dates:
Deadline for abstract submissions: 30 June 2025
Abstract acceptance notification: 1st August 2025
Registration/RSVP deadline: 10 August 2025
Workshop date: 22 August 2025
Keynote Speaker
Keynote Speaker
Dr. Ryan Sweke
Dr. Ryan Sweke
Dr. Ryan Sweke is the Alexander von Humboldt German Research Chair of Mathematics and its Applications at AIMS, as well as a senior lecturer at Stellenbosch University. He leads the Quantum at AIMS group, whose research is focused on a wide variety of topics under the broad umbrella of quantum computing and quantum information.
Title: The potential and limitations of quantum machine learning
Abstract: Recent years have witnessed an incredible interest in the potential use of quantum computers for machine learning tasks. As of yet however, it remains unclear to what extent quantum computers can enable meaningful advantages over state-of-the-art classical methods, which have also seen significant progress in the last years. In this talk I will discuss evidence for- and against the use of a variety of different quantum algorithms for machine learning, with the goal of understanding both the challenges and opportunities in the field of quantum machine learning. I will start by examining the potential and limitations of near-term friendly quantum algorithms for generative modelling. Following this, I will shift the focus to supervised learning, and discuss the extent to which a widely-used class of quantum algorithms for classification problems can be dequantized.
Organizers
Organizers
Dr. Stephanie Müller
Dr. Stephanie Müller
Dr. Stephanie Müller is a Staff Research Scientist and Technical lead for the team working on Quantum Applications at IBM Research Africa. Based in the South Africa lab, Stephanie leads a diverse team of researchers in their quest to demonstrate quantum utility in the domain of healthcare and life sciences. With degrees spanning Human Genetics, Bioinformatics, and Biomedical sciences, Stephanie is able to bridge the gap between new technologies and domain applications of global interest. Stephanie was part of the 2024 ICLR Workshops Organizing Committee and is a member of the 2025 DLIx South Africa Programme Chairs.
Dr. Nouhaila Innan
Dr. Nouhaila Innan
Dr. Nouhaila Innan is a Research Team Lead at eBRAIN Lab, Division and a Postdoctoral Associate at the Center for Quantum and Topological Systems (CQTS) at New York University (NYU) Abu Dhabi. She earned her PhD in Quantum Machine Learning from Hassan II University of Casablanca, where she also completed her Bachelor's in Physics and Master's in Physics and New Technologies, specializing in materials and nanomaterials. Her research focuses on quantum machine learning, quantum algorithms, and their applications in fields like finance and healthcare. Dr. Innan is passionate about mentoring and making quantum technologies accessible through global initiatives.
Muna Said
Muna Said
Muna Said is a Researcher at Algoverse, specializing in machine learning and quantum computing.Muna holds a BS in Computer Science from USIU-Africa. She is passionate about fostering pan-African collaborations in Artificial Intelligence and Quantum Computing through workshops, open-source toolkits, and community mentorship.
Dr. Lebohang Mashatola
Dr. Lebohang Mashatola
Dr. Lebohang Mashatola is a Research Scientist working on quantum machine learning applications at the IBM Research Africa (South Africa lab). With a background rooted in cancer biology and bioinformatics, Lebohang's research focus has evolved towards using quantum computing with the aim of incorporating his domain knowledge into quantum research at IBM, facilitating its practical application in the healthcare sector.
Dr. Aviwe Kohlakala
Dr. Aviwe Kohlakala
Dr. Aviwe Kohlakala is a Postdoctoral Research Intern in the Quantum Applications team at IBM Research Africa (South Africa lab). Aviwe’s background is rooted in a sturdy education in mathematics, with a focus in mathematical modelling and digital Science which involves areas such as digital image processing, computer vision, machine learning, and medical imaging.
Ndivhuwo Nyase
Ndivhuwo Nyase
Ndivhuwo Nyase is a Research Scientist at IBM Research Africa, working on Quantum Applications for Healthcare and Life Sciences. Based in the South Africa lab, he advances hybrid quantum-classical machine learning models for cancer research and biomarker discovery. With a strong background in Data Science, Artificial Intelligence, and Topological Data Analysis, he also leads quantum advocacy efforts, organizing meetups, workshops, and collaborations to drive the growth of quantum computing in Africa.
Yousra Farhani
Yousra Farhani
Founder of Quantum Africa, Quantum Optimization, and Machine Learning Researcher at INSA Lyon. Her focus is on developing quantum algorithms and QUBO models for NP-Hard Optimization Problems, assessing different quantum and quantum-inspired solvers.
Walid El Maouaki
Walid El Maouaki
PhD student in Quantum Machine Learning at the UH2C. Focus on enhancing the security and reliability of quantum computing, with a particular focus on critical applications in healthcare and cybersecurity. Co-founder of Quantum Africa.
Questions?
Drop us a mail and we'll respond as soon as possible: qml4africa@gmail.com
Code of Conduct
This workshop follows the Deep Learning Indaba Code of Conduct.
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