On-Chip Optical Neuromorphic Computing

TL;DR: This work demonstrates the implementation of an all-optical network scheme based on holographic coupling and induce complex spatio-temporal transients with Gigahertz bandwidth that illustrates the potential of such all- optical systems for future neural network implementations.

TL;DR: The SLH model is a general framework for open quantum systems that interact through bosonic fields, and is the basis for the quantum circuit theory developed in the text as discussed by the authors, where coherent feedback outperforms measurement-based feedback for certain linear quadratic-Gaussian (LQG) problems, and explain the discrepancy by the simultaneous utilization of both light quadratures.

TL;DR: A novel light-stimulated artificial synapse based on a graphene-nanotube hybrid phototransistor that can directly convert optical stimuli into a "neural image" for further neuronal analysis and exhibit flexible tuning of both short- and long-term plasticity.

TL;DR: In this paper, the authors developed a system that integrates color centers with photonic devices that modify their emission properties through electromagnetically tailored light and matter interaction, which can be used for communication and sensing technologies.

TL;DR: An algorithmic proof that any discrete finite-dimensional unitary operator can be constructed in the laboratory using optical devices is given, and optical experiments with any type of radiation exploring higher-dimensional discrete quantum systems become feasible.

TL;DR: In this article, a numerically stable and fairly fast scheme is described to compute the unitary matrices U and V which transform a given matrix A into a diagonal form π = U^ * AV, thus exhibiting A's singular values on π's diagonal.

TL;DR: The use of the pseudo-inverse $A^I = V\Sigma ^I U^* $ to solve least squares problems in a way which dampens spurious oscillation and cancellation is mentioned.

TL;DR: This article presents a comprehensive overview of the hardware realizations of artificial neural network models, known as hardware neural networks (HNN), appearing in academic studies as prototypes as well as in commercial use.