Quantum K-means clusteringQuantum machine learning (QML)QubitsSoft Computing - The development of noisy intermediate- scale quantum computers is expected to signify the potential advantages of quantum computing over classical computing. This paper focuses on......
图1和图2分别是MoS2-WS2多层膜异质结(非外延式异质结)在5K(图1)和150K(图2)下的二维拉曼扫描成像。扫描范围200μm*200μm,每一个像素点1μm*1μm。每一幅图片就是次的拉曼测量,这是手动测量所不敢想象的。两幅图的右侧图片是通过k-means clustering方法进行分析后得到的结果,可以清楚地看到不同温度下边...
K-means clusteringQuantum computing is one of the most promising solutions for solving optimization problems in the healthcare world. Quantum computing development aims to light up the execution of a vast and complex set of algorithmic instructions. For its implementation, the machine lea...
The Quantum k-Means algorithm encodes the input data into quantum states and uses quantum operations to perform the clustering. Here is a code snippet to initialize the problem and construct the quantum circuit for Quantum k-Means: import numpy as np from qiskit import QuantumCircuit, QuantumRegis...
模糊聚类算法在Web信息搜索中的应用 2. An improved K-means cluster algorithm 一种改进的K-均值聚类算法 3. A learning algorithm based on cluster algorithm is presented and the ratio of recognition gets improved a lot. 提出了一种基于聚类算法的选择原型向量的方法。 更多例句>> 补充...
Commonly used algorithms include K-means Clustering, Hierarchical Clustering, Hidden Markov Models. Active learning (optimal experimental design): Subfield of ML, designing algorithms able to interactively query an information source to obtain new outputs. Query strategies are typically some mix of explora...
Here, we propose and implement on the IBMQ a quantum analogue to K-means clustering, and compare it to a previously developed quantum support vector machine. We find the algorithm's accuracy comparable to the classical K-means algorithm for clustering and classification problems, and find that ...
As we have said, this is but a first, simple application of the above tools which can readily be used for other machine learning applications such as nearest neighbor classifiers or k-means clustering for unsupervised learning, where neural network techniques are not available. Let us start by ...
clustering section, we describe how the MAX-CUT problem is related to clustering and can thus be solved by adiabatic computing, quantum annealing, and the QAOA. Grover algorithm-based quantum optimization is discussed next. The quantumK-means section describes how to calculate the dot product and...
(i.e. mean coordinates of each group in the training data) closest to the point being classified. In addition to finding the Nearest Centroid,fit_and_predictalso currently supports quantum versions of k-Nearest Neighbors (regressor and classifier) as well as k-means for unsupervised learn...