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Updated: Nov 24, 2025

Generation and Coherent Control of Pulsed Quantum Frequency Combs
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Quantum-inspired canonical correlation analysis for exponentially large dimensional data.

Naoko Koide-Majima1, Kei Majima2

  • 1AI Science Research and Development Promotion Center, National Institute of Information and Communications Technology, Osaka 565-0871, Japan; Graduate School of Frontier Biosciences, Osaka University, Osaka 565-0871, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
|December 21, 2020
PubMed
Summary
This summary is machine-generated.

A new quantum-inspired algorithm (qiCCA) efficiently approximates canonical correlation analysis (CCA) for high-dimensional data. This method offers significant speedups and improved correlation extraction, especially after nonlinear data mapping.

Keywords:
Canonical correlation analysisHigh-dimensional dataMachine learningQuantum-inspired computation

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Area of Science:

  • Statistics
  • Machine Learning
  • Quantum Computing

Background:

  • Canonical Correlation Analysis (CCA) identifies relationships between datasets.
  • High-dimensional data presents computational challenges for traditional CCA.

Purpose of the Study:

  • To develop a computationally efficient CCA algorithm for high-dimensional data.
  • To leverage quantum-inspired computing for improved statistical dependency analysis.

Main Methods:

  • Proposed a quantum-inspired CCA (qiCCA) algorithm with logarithmic computational time complexity.
  • Evaluated qiCCA on synthetic and real-world datasets.
  • Applied nonlinear mapping (second-order monomials) to input data before qiCCA analysis.

Main Results:

  • qiCCA demonstrates significant computational efficiency compared to conventional CCA.
  • Nonlinear mapping followed by qiCCA extracts more correlations than linear CCA.
  • qiCCA achieves performance comparable to state-of-the-art nonlinear CCA methods.

Conclusions:

  • The proposed qiCCA algorithm is suitable for analyzing high-dimensional data.
  • Quantum-inspired computations show high potential for advancing statistical analysis of complex datasets.