IMPORTANT: WORKSHOP ATTENDANCE IS ONLY POSSIBLE FOR REGISTERED INDABA ATTENDEES
IMPORTANT: WORKSHOP ATTENDANCE IS ONLY POSSIBLE FOR REGISTERED INDABA ATTENDEES
QML4Africa returns for its second edition at Deep Learning Indaba 2026, building on the success of last year’s workshop in Kigali, Rwanda, which brought together nearly 100 participants from across the continent. This interactive workshop aims to make Quantum Computing (QC) and Quantum Machine Learning (QML) more accessible to African researchers, students, and practitioners through a combination of foundational lectures and hands-on coding sessions using IBM’s Qiskit SDK. Participants will explore core quantum concepts, practical implementations, and emerging opportunities at the intersection of AI and quantum technologies, while connecting with a growing community dedicated to advancing Africa’s quantum ecosystem.
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...)
Deadline for abstract submissions: TBA
Abstract acceptance notification: TBA
Registration/RSVP deadline: TBA
Workshop date: TBA
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.
Founder of Quantum Africa, pan-African initiative dedicated to advancing quantum technologies, empowering researchers, and expanding access to quantum education across the continent. PhD researcher at the Niels Bohr Institute, University of Copenhagen, specializing in quantum compilation and fault-tolerant quantum computing for quantum chemistry applications. Holder of an Engineering Degree and a Master’s degree in Information Technologies, with previous experience as a Quantum Machine Learning researcher working on quantum NLP and scalable quantum algorithms. Passionate about advancing quantum education, research, and inclusive scientific communities across Africa.
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.
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