Variational quantum extreme learning machine

TL;DR: In this paper , a quantum kernel logistic regression (KLR) algorithm is proposed for classification, which makes use of quantum inner product estimation to prepare the desired state and then performs quantum singular value transformation based on the block-encoding framework to obtain the optimal model parameters.

TL;DR: In this article , a quantum twin extreme learning machine (TELM) algorithm was proposed to address the problem of computationally expensive TELM for big data sets, where the swap test was used to estimate the distances from a new data point to the two hyperplanes and then make a classification in the prediction stage.

TL;DR: A new learning algorithm called ELM is proposed for feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs which tends to provide good generalization performance at extremely fast learning speed.

TL;DR: This paper introduces the basic concepts of quantum computation and quantum simulation and presents quantum algorithms that are known to be much faster than the available classic algorithms and provides a statistical framework for the analysis of quantum algorithms and quantum Simulation.

TL;DR: Las Vegas algorithms for finding discrete logarithms and factoring integers on a quantum computer that take a number of steps which is polynomial in the input size, e.g., the number of digits of the integer to be factored are given.

TL;DR: In this paper, it was shown that a quantum mechanical computer can solve integer factorization problem in a finite power of O(log n) time, where n is the number of elements in a given integer.