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Deep learning with coherent nanophotonic circuits

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TLDR
A new architecture for a fully optical neural network is demonstrated that enables a computational speed enhancement of at least two orders of magnitude and three order of magnitude in power efficiency over state-of-the-art electronics.
Abstract
Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.

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Journal ArticleDOI

Position-robust optronic convolutional neural networks dealing with images position variation

TL;DR: In this paper , the authors improved the original architecture and achieved position-robust optronic convolutional neural networks (PROPCNNs) with translation invariance through replacing strided convolution operation and fully connected operation with spectral pooling and global average pooling.
Journal ArticleDOI

Matrix eigenvalue solver based on reconfigurable photonic neural network

TL;DR: A feasible solution for the on-chip integrated photonic realization of eigenvalue solving of real-value symmetric matrices based on reconfigurable photonic neural networks is provided and lays the foundation for a new generation of intelligent on- chip integrated all-optical computing.
Journal ArticleDOI

A quantum router architecture for high-fidelity entanglement flows in quantum networks

TL;DR: In this article , a quantum router architecture comprising many quantum memories connected in a photonic switchboard is proposed to broker entanglement flows across quantum networks, and the rate and fidelity of entenglement distribution under this architecture are computed using an event-based simulator.
Posted ContentDOI

Low-loss Lithium Niobate on Insulator (LNOI) Waveguides of a 10 cm-length and a Sub-nanometer Surface Roughness

TL;DR: In this paper, a technique for realizing lithium niobate on insulator (LNOI) waveguides of a multi-centimeter-length with a propagation loss as low as 0.027 dB/cm was developed.
Posted Content

Towards Single Atom Computing via High Harmonic Generation

TL;DR: In this paper, a single-atom computer for classification problems is proposed, where parameters of the classification model are mapped to optical elements, and numerically demonstrate that this computer can successfully classify data with an accuracy that is strongly dependent on dynamical nonlinearities.
References
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Proceedings Article

ImageNet Classification with Deep Convolutional Neural Networks

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI

Deep learning

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal ArticleDOI

Human-level control through deep reinforcement learning

TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Journal ArticleDOI

Reducing the Dimensionality of Data with Neural Networks

TL;DR: In this article, an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data is described.
Journal ArticleDOI

Deep learning in neural networks

TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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