Quantum-computing with AI & blockchain: modelling, fault tolerance and capacity scheduling

Quantum-computing with AI & blockchain: modelling, fault tolerance and capacity scheduling
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人工智能量子计算

DOI:
10.1080/13873954.2019.1677725
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发表时间:
2019-10
影响因子:
1.9
通讯作者:
Wanyang Dai
Wanyang Dai
中科院分区:
数学4区
文献类型:
--
作者:
Wanyang Dai

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我们通过量子云计算和区块链对广义物联网(IoT)的硬件和软件架构进行建模。为了减少当前量子计算机和通信中的测量误差,提高量子纠缠的效率(即容错能力),我们将量子计算芯片建模为多输入多输出(MIMO)量子信道来设计量子计算芯片,并通过我们最近推导出的互信息公式获得其信道容量。为了捕获通道的内部量子比特数据流动态,我们通过深度卷积神经网络(DCNN)对其进行建模,并根据不同量子本征模或用户之间的资源竞争进行广义随机池化。池化对应于认知无线电中的两级竞争的资源分配策略:第一级是以“赢-输”的方式进行用户选择;第二级是以“赢-赢”的方式在被选择的用户之间共享资源。也就是说,我们的调度策略是一个混合鞍点的零和博弈问题和帕累托最优纳什均衡点nonzero-sumgameproblem.Theeffectivenessofourpolicyisproved.by扩散模型的理论和数值例子。
We model the hardware and software architecture for generalized Internet of Things (IoT) by quantum cloud-computing and blockchain. To reduce the measurement error and increase the efficiency of quan-tum entanglement (i.e. the capability of fault tolerance) in the current quantum computers and communications, we design a quantum-computing chip by modelling it as a multi-input multi-output (MIMO) quantum channel and obtain its channel capacity via our recently derived mutual information formula. To capture the internal qubit data flow dynamics of the channel, we model it via a deep convolutional neural network (DCNN) with generalized stochastic pooling in terms of resource-competition among different quantum eigenmodes or users. The pooling is corresponding to a resource allocation policy with two levels of competitions as in cognitive radio: the firstone is on users’ selection in a ‘win–lose’ manner; the second one is on resource-sharing among selected users in a ‘win–win’ manner. To wit, our scheduling policy is the one by mixing a saddle point to a zero-sum game problem and a Pareto optimal Nash equilibrium point to a nonzero-sumgameproblem.Theeffectivenessofourpolicyisproved.by diffusion modelling with theory and numerical examples.
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