测量量子神经网络

M. Lukac, Kamila Abdiyeva, M. Kameyama
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引用次数: 2

摘要

量子神经网络的各种模型模仿了强大的机器学习算法,广泛应用于许多智能系统和应用中。虽然存在量子神经网络的比较模型,但它们的计算复杂性可能需要特定的酉变换来模拟细胞的激活函数、模拟学习的连续过程或添加大量辅助量子位。为了解决这些问题,我们提出了一种称为CNOT测量网络(CMN)的量子神经网络模型。CMN仅使用CNOT量子门和测量算子,因此在任何量子计算机技术中都非常容易实现。CMN可以只使用这两个简单的算子,从而得到图灵通用算子AND和OR,同时保持学习速度优化到量子网络的复杂性和恒定数量的辅助量子比特。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CNOT-Measure Quantum Neural Networks
Various models of quantum neural networks exist imitating the powerful class of machine learning algorithms, widely applied and used in many of intelligent systems and applications. While comparative models of quantum neural networks exist, their computational complexity might require specific unitary transforms for simulating the activation function of the cell, simulation of continuous processes for learning or adding a large amount of ancilla qubits. In order to solve some of these problems, we present a quantum neural network model called CNOT Measured Network (CMN). The CMN uses only CNOT quantum gates and the measurement operator and as such is very simple to implement in any quantum computer technology. The CMN can by using only these two simple operators, result in a Turing universal operators AND and OR while keeping the learning speed optimized to the complex nature of the quantum network and a constant number of ancila qubits.
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