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Solving NP-complete Problems Using Quantum Weightless Neuron Nodes

机译:使用量子失重神经元节点解决NP完全问题

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Despite neural networks have super-Turing computing power, there is no known algorithm for obtaining a classical neural networks that solves NP-complete problems in polynomial time. However this paper shows that a quantum neural networks model coupled with a non-unitary operator can solve 3-SAT in polynomial time. The proposed method uses a network circuit to represent a Boolean logic function and a non-unitary operator to decide the satisfiability. The parameters of the network is set deterministically and manually, accordingly to the problem at hand with neither quantum nor classical learning.
机译:尽管神经网络具有超级图灵计算能力,但尚无用于获得经典神经网络的算法,该经典神经网络可在多项式时间内解决NP完全问题。然而,本文表明,结合非-元算子的量子神经网络模型可以在多项式时间内求解3-SAT。所提出的方法使用网络电路来表示布尔逻辑函数,并使用非-运算符来确定可满足性。网络的参数是确定性的和手动设置的,从而解决了既没有量子学习又没有经典学习的问题。

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