Ricerc@Sapienza

Classical and quantum physical systems that evolve according to an Ising Hamiltonian are currently attracting broad research interest as novel computing architectures for solving optimization problems that cannot be tackled efficiently on large scales by conventional electronics. However, all the proposed settings either involve a limited number of spins or lack of scalability.

The proposal aims at developing a novel class of photonic Ising machines that exploits the spatial degree of freedom of light for parallel processing of a vast number of spins with programmable couplings. Employing spatial light modulation technology in neural network schemes, the project intends to demonstrate a major breakthrough in the realization of high-speed and scalable novel computing hardware for hard optimization problems and machine learning implementations.

The approach is based on the idea that several thousands of spin variables can be encoded into a phase and amplitude spatial modulation of the optical wavefront. Using designed optical wave-mixing devices that couple the optical spins by linear interference, light propagation is tailored by a recurrent, neural network inspired, measurement and feedback method to evolve toward the ground state of the target spin Hamiltonian. Within this scheme - scalable in terms of spin number and practical resources - we address photonic simulation of zero-temperature ground states and phase transition phenomena for various classical spin models and solve large-scale complex optimization instances with thousands of nodes.

This multidisciplinary project combines paradigms from optics, statistical mechanics, complex systems and optimization algorithms into a cutting-edge spatial photonic setup that will lay the grounds for large-scale all-optical computing.