Control for swing-up of an inverted pendulum using qubit neural network

N. Kouda, N. Matsui, H. Nishimura
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引用次数: 13

Abstract

We have proposed a novel neuron model whose states and interactions with other neurons are based on the laws of quantum physics. In our previous works, neural network constricted by this neuron, i.e., qubit neural network, has been shown through various benchmark simulations. As an example of nonholonomic system control, in this report, the swing-up control of inverted pendulum by the qubit neural network is investigated to estimate the efficiencies for more practical applications. For comparison, our qubit neural network and a conventional neural network are evaluated. As a result, we find it is possible for our qubit neural network to swing up and stabilize the inverted pendulum while not for any conventional neural network.
用量子比特神经网络控制倒立摆的摆动
我们提出了一种新的神经元模型,其状态和与其他神经元的相互作用基于量子物理定律。在我们之前的工作中,通过各种基准模拟已经展示了由该神经元收缩的神经网络,即量子比特神经网络。作为非完整系统控制的一个例子,本文研究了用量子比特神经网络控制倒立摆的摆动,以估计其在实际应用中的效率。为了进行比较,我们对量子比特神经网络和传统神经网络进行了评估。因此,我们发现我们的量子比特神经网络有可能向上摆动并稳定倒立摆,而任何传统的神经网络都不可能。
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