Systems and methods for determining optimal parameters for dynamic quantum clustering analyses

    公开(公告)号:US10169445B2

    公开(公告)日:2019-01-01

    申请号:US14492677

    申请日:2014-09-22

    Abstract: In the present work, quantum clustering is extended to provide a dynamical approach for data clustering using a time-dependent Schrödinger equation. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering. Additionally, the parameters of the analysis can be modified in order to improve the efficiency of the dynamic quantum clustering processes.

    DEEP LEARNING DECODING OF ERROR CORRECTING CODES

    公开(公告)号:US20180357530A1

    公开(公告)日:2018-12-13

    申请号:US15996542

    申请日:2018-06-04

    CPC classification number: G06N3/0445 G06N3/084

    Abstract: A method of decoding a linear block code transmitted over a transmission channel subject to noise, comprising receiving, over a transmission channel, a linear block code corresponding to a parity check matrix, propagating the received code through a neural network of one or more decoders, the neural network having an input layer, an output layer and a plurality of hidden layers comprising a plurality of nodes corresponding to transmitted messages over a plurality of edges of a bipartite graph representation of the encoded code and a plurality of edges connecting the plurality of nodes, each edge having source node and destination nodes is assigned with a weight calculated during a training session of the neural network, the propagation follows a propagation path through the neural network dictated by respective weights of the edges and outputting a recovered version of the code according to a final output of the neural network.

    Linearized optical digital-to-analog modulator

    公开(公告)号:US10033465B2

    公开(公告)日:2018-07-24

    申请号:US15298327

    申请日:2016-10-20

    Abstract: A system for converting digital data into a modulated optical signal, comprises an electrically controllable device having M actuating electrodes. The device provides an optical signal that is modulated in response to binary voltages applied to the actuating electrodes. The system also comprises a digital-to-digital converter that provides a mapping of input data words to binary actuation vectors of M bits and supplies the binary actuation vectors as M bits of binary actuation voltages to the M actuating electrodes, where M is larger than the number of bits in each input data word. The digital-to-digital converter is enabled to map each digital input data word to a binary actuation vector by selecting a binary actuation vector from a subset of binary actuation vectors available to represent each of the input data words.

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