To solve this problem, we present a quantum algorithm for LR to implement the key task of the gradient descent method, obtaining the classical gradients in each iteration. It is shown that our algorithm achieves exponential speedup over its classical counterpart in each iteration when the dimension...
An adjusted bat algorithm has optimized SVM parameters significantly, improving classifier accuracy to 96.49% from 96.31% for the WDBC dataset42. A feature selection strategy using GA reached a 96.9% accuracy43. Comparisons among different cancer classification models like naïve Bayes, logistic regres...
The combination of Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel and AdaBoost algorithm classifiers, using nature-inspired algorithms and applying the maximum likelihood principle, has improved the stability of classification. The accuracy rates achieved are 96% for AdaBoost, 97...
Neural network state estimation for full quantum state tomography 一.引言 量子态层析(估计)(Quantum state tomography,QST)通过量子测量重建量子系统的量子态,在各种量子信息处理任务(包括量子计算和量子通信)中,对量子器件的验证和基准测试起着重要作用。QST一般由两个过程组成:系统上的量子测量(数据收集)和从...
In addition to the pipeline just described, we need to specify the layout of the hierarchical circuit, and the algorithm for learning its parameters. The circuits we use here are tree-like and can be parameterized with a simple gate-set that is compatible with currently available quantum compute...
Benz SA, Weibel R. 2014. Road network selection for medium scales using an extended stroke-mesh combination algorithm.Cartogr Geogr Inf Sci. 41(4):323–339. doi: 10.1080/15230406.2014.928482. Web of Science ®Google Scholar Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Llo...
for larger multi-qubit systems: training classical machine learning algorithms on lower-order qubit systems has the benefit of enabling researchers to consider how such algorithms can or may learn multi-qubit relations which in turn can assist in algorithm design when applied to higher-order systems...
The study presents a novel approach using MFF-HistoNet to improve breast cancer diagnosis and grading. The algorithm combines CNN with a QTN and traditional texture feature extraction methods, such as Gray-Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), and Gabor filtering. The ...
Algorithm 1 outlines the training process for the quantum discriminator. The inputs to the model are the feature vectors\hat{x}_1, \hat{x}_2, \ldots \hat{x}_N, and the training labelsy_1, y_2, \ldots , y_N. We first initializebandB. The\texttt {length}(z)function computes ...
for the parameters20. Optimizing the two key parameters of KELM using meta-heuristic algorithms can help quickly find suitable parameter combinations and improve the model’s classification performance21. For instance, Lu et al.16utilized the Active Operators Particle Swarm Optimization algorithm (APSO)...