Experimental Quantum Stochastic Walks Simulating Associative Memory of Hopfield Neural Networks

Tang, Hao; Feng, Zhen; Wang, Yinghan; Lai, Peng-Cheng; Wang, Chaoyue; Ye, Zhuo-Yang; Wang, Cheng-Kai; Shi, Ziyu; Wang, Tianyu; Chen, Yuan; Gao, Jun; Jin, Xian

doi:10.1103/physrevapplied.11.024020

Cited by 25 publications

(11 citation statements)

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“…3, which can be reflected into a change in propagation constant. Our thoery, in good agreement with previous experiments [48,49], shows that this change is linearly dependent on the energy variation of the writing laser [50].…”

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confidence: 92%

Vector Vortex Beam Emitter Embedded in a Photonic Chip

et al. 2020

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Vector vortex beams simultaneously carrying spin and orbital angular momentum of light promise additional degrees of freedom for modern optics and emerging resources for both classical and quantum information technologies. The inherently infinite dimensions can be exploited to enhance data capacity for sustaining the unprecedented growth in big data and internet traffic, and can be encoded to build quantum computing machines in high-dimensional Hilbert space. So far much progress has been made in the emission of vector vortex beams from a chip surface into free space, however, the generation of vector vortex beams inside a photonic chip hasn't been realized yet. Here, we demonstrate the first vector vortex beam emitter embedded in a photonic chip by using femtosecond laser direct writing. We achieve a conversion of vector vortex beams with an efficiency up to 30% and scalar vortex beams with an efficiency up to 74% from Gaussian beams. We also present an expanded coupled-mode model for understanding the mode conversion and the influence of the imperfection in fabrication. The fashion of embedded generation makes vector vortex beams directly ready for further transmission, manipulation and emission without any additional interconnection. Together with the ability to be integrated as an array, our results may enable vector vortex beams become accessible inside a photonic chip for high-capacity communication and high-dimensional quantum information processing.

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confidence: 92%

Vector Vortex Beam Emitter Embedded in a Photonic Chip

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“…It essentially replaces the classical random walks in Google PageRank with quantum stochastic walks 6 . Such a model of flexibly mixing classical random walk and quantum walk 8 already has wide applications in energy transport problems 9 , associative memory in Hopfield neural networks 10,11 , decision making 12 , and more issues in open quantum systems. Applying quantum stochastic walk to quantum PageRank, a few advantages over classical PageRank have been demonstrated 6 , for instance, to generate more accurate ranking by reducing degeneracy from elements of the same probability, and to have better notification of the significance of secondary-hubs in the network, etc.…”

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confidence: 99%

TensorFlow Solver for Quantum PageRank in Large-Scale Networks

Tang¹,

He²,

Shi³

et al. 2020

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Google PageRank is a prevalent and useful algorithm for ranking the significance of nodes or websites in a network, and a recent quantum counterpart for PageRank algorithm has been raised to suggest a higher accuracy of ranking comparing to Google PageRank. The quantum PageRank algorithm is essentially based on quantum stochastic walks and can be expressed using Lindblad master equation, which, however, needs to solve the Kronecker products of an O(N 4 ) dimension and requires severely large memory and time when the number of nodes N in a network increases above 150. Here, we present an efficient solver for quantum PageRank by using the Runge-Kutta method to reduce the matrix dimension to O(N 2 ) and employing TensorFlow to conduct GPU parallel computing. We demonstrate its performance in solving quantum PageRank for the USA major airline network with up to 922 nodes. Compared with the previous quantum PageRank solver, our solver dramatically reduces the required memory and time to only 1% and 0.2%, respectively, making it practical to work in a normal computer with a memory of 4-8 GB in no more than 100 seconds. This efficient solver for large-scale quantum PageRank and quantum stochastic walks would greatly facilitate studies of quantum information in real-life applications.

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“…Roth [75] propose an iterative retraining approach using RNNs for simulating bulk quantum systems via mapping translations of lattice vectors to the RNN time index. Hopfield Networks [35] were a popular early form of a recurrent NN for which several works [70,92,76] have proposed quantum variants.…”

Section: Quantum Rnnsmentioning

confidence: 99%

Advances in Quantum Deep Learning: An Overview

Garg,

Ramakrishnan

2020

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The last few decades have seen significant breakthroughs in the fields of deep learning and quantum computing. Research at the junction of the two fields has garnered an increasing amount of interest, which has led to the development of quantum deep learning and quantum-inspired deep learning techniques in recent times. In this work, we present an overview of advances in the intersection of quantum computing and deep learning by discussing the technical contributions, strengths and similarities of various research works in this domain. To this end, we review and summarise the different schemes proposed to model quantum neural networks (QNNs) and other variants like quantum convolutional networks (QCNNs). We also briefly describe the recent progress in quantum inspired classic deep learning algorithms and their applications to natural language processing.

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Experimental Quantum Stochastic Walks Simulating Associative Memory of Hopfield Neural Networks

Cited by 25 publications

References 46 publications

Vector Vortex Beam Emitter Embedded in a Photonic Chip

Vector Vortex Beam Emitter Embedded in a Photonic Chip

TensorFlow Solver for Quantum PageRank in Large-Scale Networks

Advances in Quantum Deep Learning: An Overview

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