Quantum regularized least squares solver with parameter estimate

DOI10.1007/s11128-020-2615-9OpenAlexW3006992363MaRDI QIDQ2677271

Changpeng Shao, Hua Xiang

Publication date: 13 January 2023

Published in: Quantum Information Processing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1812.09934

zbMATH Keywords

Tikhonov regularizationquantum algorithmL-curveHanke-Raus ruleGrover's searchregularization parameter estimate

Mathematics Subject Classification ID

Quantum computation (81P68) Numerical solution to inverse problems in abstract spaces (65J22)

Related Items (2)

Quantum radial basis function method for scattered data interpolation ⋮ Quantum locality preserving projection algorithm

Uses Software

• Regularization tools

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Fast quantum algorithms for least squares regression and statistic leverage scores
• Regularization tools: A Matlab package for analysis and solution of discrete ill-posed problems
• Estimation techniques for distributed parameter systems
• Regularization with randomized SVD for large-scale discrete inverse problems
• Randomized algorithms for large-scale inverse problems with general Tikhonov regularizations
• Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
• Analysis of Discrete Ill-Posed Problems by Means of the L-Curve
• The Use of the L-Curve in the Regularization of Discrete Ill-Posed Problems
• Quantum Algorithm for Systems of Linear Equations with Exponentially Improved Dependence on Precision
• A General Heuristic for Choosing the Regularization Parameter in Ill-Posed Problems
• Discrete Inverse Problems

This page was built for publication: Quantum regularized least squares solver with parameter estimate