Perceptrons with Hebbian learning based on wave ensembles in plastic potentials

Abstract

A general scheme to realize a perceptron for hardware neural networks is presented, where multiple interconnections are achieved by a superposition of Schrodinger 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 non-linear optical system; 2) optical systems patterned by optical lithography; and 3) exciton-polariton systems with phonon or nuclear spin interactions.

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• Exciton-polariton condensates
• Spin torque building blocks
• All-optical polariton transistor
• Knight Field Enabled Nuclear Spin Polarization in Single Quantum Dots
• Exciton-polariton mediated superconductivity
• Optical pumping of quantum dot nuclear spins
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• Light Engineering of the Polariton Landscape in Semiconductor Microcavities
• A complete architecture of integrated photonic circuits based on AND and NOT logic gates of exciton-polaritons in semiconductor microcavities
• Bistability of the Nuclear Polarisation created through optical pumping in InGaAs Quantum Dots
• Interference of Degenerate Polariton Condensates in Microcavities
• Local tuning of photonic crystal cavities using chalcogenide glasses
• Subwavelength modulational instability and plasmon oscillons in nanoparticle arrays
• Motion of spin polariton bullets in semiconductor microcavities
• Lifetime of sub-THz coherent acoustic phonons in a GaAs-AlAs superlattice
• Optically erasing disorder in semiconductor microcavities with dynamic nuclear polarization

Cited by in corpus (9)

• Continuous-variable quantum neural networks
• Is quantum advantage the right goal for quantum machine learning?
• Self-interfering wavepackets
• Exciton-Polariton Quantum Gates Based on Continuous Variables
• Energy-efficient neural network inference with microcavity exciton-polaritons
• Superpolynomial Quantum Enhancement in Polaritonic Neuromorphic Computing
• Neural networks based on ultrafast time-delayed effects in exciton-polaritons
• Self-organization of network dynamics into local quantized states
• No one-hidden-layer neural network can represent multivariable functions