Neuromorphic computing in Ginzburg-Landau polariton lattice systems

Abstract

The availability of large amounts of data and the necessity to process it efficiently have led to rapid development of machine learning techniques. To name a few examples, artificial neural network architectures are commonly used for financial forecasting, speech and image recognition, robotics, medicine, and even research. Direct hardware for neural networks is highly sought for overcoming the von Neumann bottleneck of software implementations. Reservoir computing (RC) is a recent and increasingly popular bio-inspired computing scheme which holds promise for an efficient temporal information processing. We demonstrate the applicability and performance of reservoir computing in a general complex Ginzburg-Landau lattice model, which adequately describes dynamics of a wide class of systems, including coherent photonic devices. In particular, we propose that the concept can be readily applied in exciton-polariton lattices, which are characterized by unprecedented photonic nonlinearity, opening the way to signal processing at rates of the order of 1 Tbit (1/s).

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Cited by in corpus (21)

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• Non-reciprocal transport of Exciton-Polaritons in a non-Hermitian chain
• Universal power law decay in the dynamic hysteresis of an optical cavity with non-instantaneous photon-photon interactions
• From the topological spin-Hall effect to the non-Hermitian skin effect in an elliptical micropillar chain
• Energy-efficient neural network inference with microcavity exciton-polaritons
• Tunable optical nonlinearity for TMD polaritons dressed by a Fermi sea
• Noisy Quantum Kernel Machines
• Emergence of micro-frequency comb via limit cycles in dissipatively coupled condensates
• Superpolynomial Quantum Enhancement in Polaritonic Neuromorphic Computing
• Optically induced topological spin-valley Hall effect for exciton polaritons
• Microscopic theory of exciton and trion polaritons in doped monolayers of transition metal dichalcogenides
• Polariton lattices as binarized neuromorphic networks
• Photonic kernel machine learning for ultrafast spectral analysis
• Neural networks based on ultrafast time-delayed effects in exciton-polaritons
• The Roles of Kerr nonlinearity in a Bosonic Quantum Neural Network
• Theory of nonlinear excitonic response of hybrid organic perovskites in the regime of strong light-matter coupling
• Machine learning via relativity-inspired quantum dynamics
• Polaritonic Machine Learning for Graph-based Data Analysis
• Quantization of Polaritons Confined in Dielectric Structures