Associate Professor Sally Shrapnel

Researcher biography
Dr Sally Shrapnel is an internationally recognised interdisciplinary scientist whose research spans quantum physics, artificial intelligence, digital medicine, and philosophy. With a unique career trajectory bridging clinical medicine and cutting-edge quantum technologies, Dr Shrapnel is committed to solving foundational and applied problems that cross traditional disciplinary boundaries.
A registered medical practitioner and Fellow of the Royal Australian College of General Practitioners, she brings over two decades of clinical experience in Tasmania, Queensland, and the UK. After earning an MSc in Bioengineering from Imperial College London, she pursued a PhD in Quantum Artificial Intelligence—focusing on quantum causal inference—which launched her second career as a quantum physicist.
Currently, Dr Shrapnel is Associate Professor of Physics at The University of Queensland and Deputy Director of the ARC Centre of Excellence for Engineered Quantum Systems (EQUS). Her research addresses two fundamental questions:
- What does quantum theory reveal about the nature of reality?
- Can quantum resources be harnessed to design faster, more efficient AI algorithms?
These inquiries drive her leading contributions in Quantum Foundations and Quantum Machine Learning, where she develops novel theoretical frameworks and algorithms that aim to unlock the quantum advantage in emerging technologies. As Program Lead for Quantum Technologies for Health at the Queensland Digital Health Centre, Dr Shrapnel is also preparing the state's healthcare ecosystem for the next technological revolution—bringing quantum tools into real-world applications in health and medicine.
A passionate advocate for interdisciplinary research, Dr Shrapnel continues to publish widely across quantum physics, computer science, digital health, and the philosophy of science. Her work exemplifies the power of rigorous, cross-disciplinary thinking to address some of the most profound and practical challenges of our time.
Featured projects | Duration |
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Machine Learning for acute kidney injury in COVID 19 | |
Interpretable machine learning models for medical imaging |