The many faces of optical neural networks

by Alex Ivovosky (Oxford University)

Friday 14 Mar 2025, 16:00 → 17:00 Europe/Rome

Aula 7 (Dipartimento di Fisica-Ed. Fermi)

Optical neural networks (ONNs) harness the fundamental properties of light to enable ultrafast, energy-efficient computation, surpassing the limitations of digital-electronic systems in tasks such as large-scale matrix multiplications. By exploiting interference, diffraction, and nonlinearity, ONNs can perform parallel processing of high-dimensional data, reducing latency and power consumption. This talk explores three complementary perspectives on ONNs. First, we discuss ONN applications for machine learning and our recent results on training an ONN by propagating light backwards through its layers. Second, we show how an ONN can be applied for spatial mode decomposition of an optical field, enabling the extraction of spatial information beyond the classical diffraction limit by leveraging the quantum and classical correlations in the field. Finally, we discuss coherent Ising machines, in which an optical network with feedback finds minimum-energy gates of interacting spin systems to solve nontrivial combinatorial optimization problems. In particular, we highlight our recent findings on polarization symmetry breaking, offering a new mechanism for all-optical implementation of an Ising machine. Together, these facets illustrate the versatility and transformative potential of ONNs.