Quantum Circuit Design For Quantum Neural Unit And Physical Properties Of Coupled Resonators

Quantum machine learning is a hot research field in quantum information science.Studies shows that the Quantum computing and quantum simulation can greatly improve the efficiency of machine learning.Many quantum algorithms have been proposed,such as support vector machine?SVM?,principal component analysis algorithm.These results greatly promote the theoretical and experimental physics of quantum computing system,especially quantum information processing.This paper focus on the quantum network model to study two types of design problems,including quantum circuit Design for a quantum neural unit,as well as the microcavity singular point design.The research contents are as follows:?1?Based on^[1],we propose a model of duality perceptron in the dual space,and the complexity analysis is carried out to prove that the computational complexity of the average distribution of the average distribution is lower than that used by Wiebe.According to the quantum Fourier transform,the arithmetic module and complement module are used to realize the inner product operation of the data.The feasibility of the circuit is demonstrated by a testing example using the IBM-Q cloud quantum computer,where 16 qubits are used to classify four data samples.?2?In order to implement quantum algorithms,controlling the transmission of quan-tum information in the network is very important.This paper studies the how to design singularity Point,in microcavity network.All possible EP for the three-cavity network system are classified according to the strength of the coupling,which includes not only the reported cases in the literature but also the EP points in a new type of circular chain network.The paper gives all possible EP including how to calculate the coupling param-eter.The singularity of the signal transmission near EP point is verified by simulation experiment.