Photonic Ising machines and quantum neural networks
#Quantum
#Entanglement
#PhaseEstimation
#QuantumComputing
Abstract:
Artificial intelligence and combinatorial optimization problems—such as drug discovery and prime factorization—remain challenging even for advanced computers. We are attempting to address these limitations by building photonic processors inspired by the brain—photonic neural networks—which utilize light for faster and more energy-efficient processing [1]. We will discuss photonic networks, including Ising machines enabled by thin-film lithium niobate photonics [2], highlighting their applications in number partitioning, protein folding, wireless communications, and deep learning. Time permitting, we will briefly introduce a quantum photonic neural network that can learn to act as near-perfect components of quantum technologies and discuss the role of weak nonlinearities [3].
[1] Shastri, B.J. et al. Photonics for artificial intelligence and neuromorphic computing. Nature Photonics 15 (2021)
[2] Al-Kayed, N. et al. Programmable 200 GOPS Hopfield-inspired photonic Ising machine. Nature 648 (2025)
[3] Ewaniuk, J et al. Imperfect quantum photonic neural networks. Advanced Quantum Technologies (2023)
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Date and Time
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J. Armand Bombardier J-1035, Polytechnique Montréal
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Montréal, Quebec
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Canada
H3T 1J4
- Starts
11 February 2026 06:00 PM UTC
- Ends
24 April 2026 01:00 PM UTC
- No Admission Charge
Speakers
Bhavin J. Shastri of Queen’s University
Topic:
Photonic Ising machines and quantum neural networks
Abstract:
Artificial intelligence and combinatorial optimization problems—such as drug discovery and prime factorization—remain challenging even for advanced computers. We are attempting to address these limitations by building photonic processors inspired by the brain—photonic neural networks—which utilize light for faster and more energy-efficient processing [1]. We will discuss photonic networks, including Ising machines enabled by thin-film lithium niobate photonics [2], highlighting their applications in number partitioning, protein folding, wireless communications, and deep learning. Time permitting, we will briefly introduce a quantum photonic neural network that can learn to act as near-perfect components of quantum technologies and discuss the role of weak nonlinearities [3].
[1] Shastri, B.J. et al. Photonics for artificial intelligence and neuromorphic computing. Nature Photonics 15 (2021)
[2] Al-Kayed, N. et al. Programmable 200 GOPS Hopfield-inspired photonic Ising machine. Nature 648 (2025)
[3] Ewaniuk, J et al. Imperfect quantum photonic neural networks. Advanced Quantum Technologies (2023)
Biography:
Short Bio: Prof. Shastri is a Canada Research Chair in Neuromorphic Photonic Computing, an Associate Professor of Engineering Physics at Queen’s University, Canada, and the Scientific Co-Director of NUCLEUS, a pan-Canadian photonic computing program. He earned his Ph.D. in Electrical Engineering (Photonics) from McGill University in 2012 and was a Banting Postdoctoral Fellow and Associate Research Scholar at Princeton University. In 2024, Dr. Shastri was inducted into the Royal Society of Canada as a Member of the College. He is a 2025 Alfred P. Sloan Research Fellow in Physics and, in 2024, was recognized by Science News as one of its 10 Scientists to Watch. He received the 2022 SPIE Early Career Achievement Award and the 2020 Early Scientist Prize in Optics from the International Commission of Optics (ICO). He is a co-author of the book Neuromorphic Photonics (Taylor & Francis, 2017).
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