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) .

Co-sponsored by: Prof. Nicolas Quesada

Speaker(s): Bhavin J. Shastri

J. Armand Bombardier J-1035, Polytechnique Montréal, Montréal, Quebec, Canada, H3T 1J4