University of Waterloo
200 University Avenue West
Waterloo, Ontario, Canada N2L 3G1
Phone: (519) 888-4567 ext 32215
Fax: (519) 746-8115
Dr. Ronagh’s research interests involve algorithmic aspects of quantum computation. He explores novel applications of quantum computation by designing and analysing quantum algorithms for solving computational challenges wherein the classical state of the art is costly machine learning and high-performance computing. The symbiotic relationship between classical and quantum computing is not only important in unlocking applications of quantum computers but also crucial in building them. Dr. Ronagh is therefore also interested in using optimization and control theory to improve the hybrid quantum-classical schemes used for quantum control, quantum error correction, and fault-tolerant quantum computation.
Dr. Ronagh's current research program focuses on using quantum simulation and computation to devise more efficient methods for representational and reinforcement learning. The mechanisms of learning in existing machine learning algorithms are far from that of humans and animals. This observation has motivated Dr. Ronagh to explore alternative models of learning (e.g. energy-based models) that may have better resemblance to the neurocognitive systems of humans and animals. Training such models require simulation of complex physical systems and thermodynamic properties of them which appear to be classically interactive. Dr. Ronagh explores using quantum computation and its capabilities in simulating physical systems to design improved intelligence systems. These potential improvements include sample efficiency in learning, avoiding critical dependence on labelled data, lower power consumption in training and deployment, robustness to noisy feedback from the environment, and immunity to adversarial attacks.
Finding the ground state of spin Hamiltonians with reinforcement learning. Nature Machine Intelligence. 2(9):509—517 (2020)
The inertia operator on the motivic Hall algebra. Compositio Mathematica. 155(3): 528—598 (2019)
Deep neural decoders for near term fault-tolerant experiments. Quantum Science and Technology. 3(4):044002 (2018)
Reinforcement learning using quantum Boltzmann machines. Quantum Information and Computation. 18(1-2):0051—0074 (2018)
Please see Google scholar for a complete list of Professor Ronagh’s publications.
2016 PhD Mathematics, University of British Columbia, Vancouver, Canada
2011 MSc Mathematics, University of British Columbia, Vancouver, Canada
2009 BSc Mathematics, Sharif University of Technology, Tehran, Iran
2009 BSc Computer Science, Sharif University of Technology, Tehran, Iran
The University of Waterloo acknowledges that much of our work takes place on the traditional territory of the Neutral, Anishinaabeg and Haudenosaunee peoples. Our main campus is situated on the Haldimand Tract, the land granted to the Six Nations that includes six miles on each side of the Grand River. Our active work toward reconciliation takes place across our campuses through research, learning, teaching, and community building, and is centralized within our Office of Indigenous Relations.