An improved quantum principal component analysis algorithm based on the quantum singular threshold method

DOI10.1016/j.physleta.2019.06.026zbMath1476.81041OpenAlexW2950631233WikidataQ127679469 ScholiaQ127679469MaRDI QIDQ2232438

Tan Li, Wan-Su Bao, Jie Lin, Shuo Zhang, Xiang Wang

Publication date: 5 October 2021

Published in: Physics Letters. A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physleta.2019.06.026

zbMATH Keywords

principle component analysissingular value threshold method

Mathematics Subject Classification ID

Eigenvalues, singular values, and eigenvectors (15A18) Selfadjoint operator theory in quantum theory, including spectral analysis (81Q10) Numerical methods for eigenvalue problems for boundary value problems involving PDEs (65N25) Compactifications; symmetric and spherical varieties (14M27)

Related Items (3)

On new PageRank computation methods using quantum computing ⋮ Quantum-enhanced least-square support vector machine: simplified quantum algorithm and sparse solutions ⋮ Quantum locality preserving projection algorithm

Cites Work

• Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression
• Efficient quantum algorithms for simulating sparse Hamiltonians
• Subspace Sampling and Relative-Error Matrix Approximation: Column-Based Methods
• Probabilistic Principal Component Analysis
• Improved reversible and quantum circuits for Karatsuba-based integer multiplication.
• An efficient quantum algorithm for spectral estimation

This page was built for publication: An improved quantum principal component analysis algorithm based on the quantum singular threshold method