2014/08/31 by T. Espinosa-Ortega, T. C. H. Liew
Computer Science · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Computer science #Hebbian theory #Mechanical and Optical Resonators #Neural Networks and Reservoir Computing #Perceptron #Physics #Quantum mechanics #Semiconductor Quantum Structures and Devices #Statistical physics #Superposition principle #cond-mat.dis-nn #cs.ET
paper · pdf · doi:10.1103/physrevlett.114.118101
published as Phys. Rev. Lett. 114, 118101 (2015)
arxiv created 2015/02/25 · openalex publication_date 2015/03/18 · arxiv updated 2015/06/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
A general scheme to realize a perceptron for hardware neural networks is presented, where multiple interconnections are achieved by a superposition of Schrödinger waves. Spatially patterned potentials process information by coupling different points of reciprocal space. The necessary potential shape is obtained from the Hebbian learning rule, either through exact calculation or construction from a superposition of known optical inputs. This allows implementation in a wide range of compact optical systems, including (1) any nonlinear optical system, (2) optical systems patterned by optical lithography, and (3) exciton-polariton systems with phonon or nuclear spin interactions.