When
Where
Title
Computing with Physical Systems: Opportunities and Fundamental Limits
Abstract
Recent strides in machine learning have shown that computation can be performed by practically any controllable physical system that responds to physical stimuli encoding data [1]. This perspective opens new frontiers for computational approaches using Physical Neural Networks (PNNs) [2, 3, 4] and provides a framework to deepen our understanding of their biological counterparts—neural circuits in living organisms. To fully leverage this potential, PNNs must be trained with a nuanced awareness of the physical nature of signal and noise, where signal is defined relative to the specific computational task. This perspective aligns closely with approaches to determining fundamental limits in sensing but extends these ideas to a new level to encompass broader computational opportunities. I will share some perspectives on how we approach this new domain of inquiry and some recent results.
Based on work with Fangjun Hu, Saeed A. Khan, Gerasimos Angelatos, Marti Vives, Esin Türeci, Graham E. Rowlands, Guilhem J. Ribeill, Nicholas Bronn.
[1] Aspen Center for Physics Winter Conference, Computing with Physical Systems, https://computingwithphysicalsystems.com/2024/
[2] F. Hu et al. `Tackling Sampling Noise in Physical Systems for Machine Learning Applications: Fundamental Limits and Eigentasks." Phys. Rev. X 13, 041020 (2023).
[3] S. A. Khan et al., `A neural processing approach to quantum state discrimination", arxiv:2409.03748.
[4] F. Hu et al. `Overcoming the Coherence Time Barrier in Quantum Machine Learning on Temporal Data", Nature Commun. 15, 7491 (2024).
Bio
Hakan E. Türeci is a Professor of Electrical and Computer Engineering at Princeton University. He earned his undergraduate degree in Physics from Bilkent University and completed his Ph.D. in Physics at Yale University. Following postdoctoral research at Yale and ETH Zurich, he became an Assistant Professor of Physics at ETH Zurich before joining Princeton University in 2010. His research primarily focuses on non-equilibrium dynamics in classical and quantum optical collective systems, with recent focus on quantum electrodynamics of superconducting devices, the physics of computation, and computing with physical systems.
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