Patent
Programmable photonic processing
Jacob Mower,Nicholas C. Harris,Dirk Englund,Greg Steinbrecher +3 more
- 26 Sep 2017
7
TL;DR: A programmable photonic integrated circuit as discussed by the authors implements arbitrary linear optics transformations in the spatial mode basis with high fidelity under a realistic fabrication model, and it is shown that programmability dramatically improves device tolerance to fabrication imperfections and enables a single device to implement a broad range of both quantum and classical linear optics experiments.
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Abstract: A programmable photonic integrated circuit implements arbitrary linear optics transformations in the spatial mode basis with high fidelity. Under a realistic fabrication model, we analyze programmed implementations of the CNOT gate, CPHASE gate, iterative phase estimation algorithm, state preparation, and quantum random walks. We find that programmability dramatically improves device tolerance to fabrication imperfections and enables a single device to implement a broad range of both quantum and classical linear optics experiments. Our results suggest that existing fabrication processes are sufficient to build such a device in the silicon photonics platform.
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Citations
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Optical Ising Machines and Optical Convolutional Neural Networks
Charles Roques-Carmes,Yichen Shen,Li Jing,Tena Dubček,Scott Skirlo,Hengameh Bagherianlemraski,Marin Soljacic +6 more
- 11 Jul 2018
TL;DR: In this article, a photonic parallel network is proposed to find the ground state of a general Ising problem and probe critical behaviors of universality classes and their critical exponents.
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Tomo Lazovich,Darius Bunandar,Nicholas C. Harris,Forsythe Martin +3 more
- 16 Jan 2020
TL;DR: In this paper, the authors propose a method for training a matrix-based differentiable program using a photonics-based processor, which includes at least one matrix-valued variable associated with a matrix of values in a Euclidean vector space.
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Adjustment of qubit frequency through annealing
Jason S. Orcutt,Sami Rosenblatt +1 more
- 28 Nov 2017
TL;DR: In this paper, a method and device for forming a multi-qubit chip is described, where each qubit comprises a Josephson junction and annealing is performed by one or more of a plurality of laser emission sources on a planar lightwave circuit.
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Photonic processing systems and methods
Darius Bunandar,Nicholas C. Harris,Carl Ramey +2 more
- 14 May 2019
TL;DR: In this article, a photonic processing system, which consists of an encoder, a receiver and a matrix-vector multiplication operator, is described, where the encoder encodes an input vector into a first plurality of optical signals and the receiver outputs an electrical digital representation of the output vector.
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Real-number photonic encoding
Michael N. Gould,Darius Bunandar,Shashank Gupta,Nicholas C. Harris +3 more
- 14 May 2019
TL;DR: Optical encoders for encoding signed, real numbers using optical fields are described in this article, where the optical fields may be detected using coherent detection, without the need for independent phase and amplitude control.
3
References
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.
67K
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
- 01 Jan 1998
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
53.5K
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
46.9K
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
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.
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