Výsledky vyhledávání - "Huang, Jin‐Long"

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Training via quantum superposition circumventing local minima and vanishing gradient of sinusoidal neural network

Autor: Wen, Zujin, Huang, Jin-Long, Dahlsten, Oscar

Deep neural networks have been very successful in applications ranging from computer vision and natural language processing to strategy optimization in games. Recently neural networks with sinusoidal activation functions (SinNN) were found to be idea

Externí odkaz: http://arxiv.org/abs/2410.22016

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Remote detectability from entanglement bootstrap I: Kirby's torus trick

Autor: Shi, Bowen, Huang, Jin-Long, McGreevy, John

Remote detectability is often taken as a physical assumption in the study of topologically ordered systems, and it is a central axiom of mathematical frameworks of topological quantum field theories. We show under the entanglement bootstrap approach

Externí odkaz: http://arxiv.org/abs/2301.07119

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Interpretable Quantum Advantage in Neural Sequence Learning

Autor: Anschuetz, Eric R., Hu, Hong-Ye, Huang, Jin-Long, Gao, Xun

Publikováno v: PRX Quantum 4, 020338 (2023)

Quantum neural networks have been widely studied in recent years, given their potential practical utility and recent results regarding their ability to efficiently express certain classical data. However, analytic results to date rely on assumptions

Externí odkaz: http://arxiv.org/abs/2209.14353

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Knots and entanglement

Autor: Huang, Jin-Long, McGreevy, John, Shi, Bowen

We extend the entanglement bootstrap approach to (3+1)-dimensions. We study knotted excitations of (3+1)-dimensional liquid topological orders and exotic fusion processes of loops. As in previous work in (2+1)-dimensions, we define a variety of super

Externí odkaz: http://arxiv.org/abs/2112.08398

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