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Guest Post: QML4Africa Highlights Growth of Africa’s Quantum Research Community

Guest Post by Somtochukwu Igwegbe, African Quantum Consortium Correspondent, Research & Development, Quantum Africa. Quantum computing hardware is being built almost entirely outside Africa, in laboratories across North America, Europe,…

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Publisher The Quantum Insider

Sep 7, 2026 at 10:27 AM UTC · Updated il y a 2 jours · 4 min de lecture

Guest Post: QML4Africa Highlights Growth of Africa’s Quantum Research Community
Image via The Quantum Insider

Key Signal

>1,000 Indaba attendees

Last Updated

il y a 2 jours

Traduction…

Guest Post by Somtochukwu Igwegbe, African Quantum Consortium Correspondent, Research & Development, Quantum Africa.

Quantum computing hardware is being built almost entirely outside Africa, in laboratories across North America, Europe, and Asia. That fact has shaped how the continent is usually written into the story: as a market to eventually reach, not a place doing the research. The second edition of the Quantum Machine Learning for Africa (QML4Africa) workshop, organized by Quantum Africa and held in August as part of Deep Learning Indaba 2026 in Lagos, Nigeria, was built around a different premise: that African researchers should be building with quantum tools now, while the field is still taking shape, rather than waiting to be introduced to it later.

I attended the workshop as part of its organizing team, moving between sessions and conversations over the course of the day.

From Kigali to Lagos

QML4Africa began a year earlier in Kigali, Rwanda, as part of Deep Learning Indaba 2025. Organizers billed it at the time as the first QML workshop dedicated to the African continent, organized by Quantum Africa, with instruction led by researchers from IBM Research Africa and New York University Abu Dhabi. That inaugural edition was structured as a half-day, hands-on programme built around IBM’s Qiskit programming framework, with that year’s focus on using quantum machine learning models for cancer classification from histopathology images.

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