Pooya Ronagh
Biography
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.
Research Interests
- Quantum Science
- Quantum Simulation
- Quantum algorithms
- Optimization and control
- Machine learning
- Reinforcement learning
Scholarly Research
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.
Education
- 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
Awards
- 2009 Benjamin Franklin Fellowship, University of Pennsylvania
Affiliations and Volunteer Work
- Research Assistant Professor, Institute for Quantum Computing
- Scientific Lead, Perimeter Institute Quantum Intelligence Lab
Teaching*
- PHYS 449 - Machine Learning in Physics
- Taught in 2021, 2022, 2023
- PHYS 490 - Special topics in Physics
- Taught in 2020, 2021
* Only courses taught in the past 5 years are displayed.
Selected/Recent Publications
- 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)