Journal Article10.1016/j.physa.2023.128454
Quantum kernel logistic regression based Newton method
Andrea Kern
- 01 Feb 2023
Vol. 611, pp 128454-128454
3
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.
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Abstract: Kernel logistic regression (KLR) is a powerful machine learning model for classification, which has wide applications in pattern recognition. However, classical KLR algorithm is computationally expensive when dealing with big data sets. Since quantum technique exhibits a computational advantages in tackling machine learning problems, we devise a quantum KLR algorithm. Specifically, our algorithm 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. It is theoretically demonstrated that our algorithm has an exponential speedup over its classical counterpart.
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TL;DR: This work exhibits a quantum algorithm for estimating x(-->)(dagger) Mx(-->) whose runtime is a polynomial of log(N) and kappa, and proves that any classical algorithm for this problem generically requires exponentially more time than this quantum algorithm.
Quantum Amplitude Amplification and Estimation
TL;DR: In this article, the amplitude amplification algorithm was proposed to find a good solution after an expected number of applications of the algorithm and its inverse which is proportional to a factor proportional to 1/a.
Quantum Support Vector Machine for Big Data Classification
TL;DR: This work shows that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples, and an exponential speedup is obtained.
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