Eigen component analysis: A quantum theory incorporated machine learning
technique to find linearly maximum separable components
- URL: http://arxiv.org/abs/2003.10199v3
- Date: Fri, 3 Apr 2020 13:24:30 GMT
- Title: Eigen component analysis: A quantum theory incorporated machine learning
technique to find linearly maximum separable components
- Authors: Chen Miao, Shaohua Ma
- Abstract summary: In quantum mechanics, a state is the superposition of multiple eigenstates.
We propose eigen component analysis (ECA), an interpretable linear learning model.
ECA incorporates the principle of quantum mechanics into the design of algorithm design for feature extraction, classification, dictionary and deep learning.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: For a linear system, the response to a stimulus is often superposed by its
responses to other decomposed stimuli. In quantum mechanics, a state is the
superposition of multiple eigenstates. Here, by taking advantage of the phase
difference, a common feature as we identified in data sets, we propose eigen
component analysis (ECA), an interpretable linear learning model that
incorporates the principle of quantum mechanics into the design of algorithm
design for feature extraction, classification, dictionary and deep learning,
and adversarial generation, etc. The simulation of ECA, possessing a measurable
$class\text{-}label$ $\mathcal{H}$, on a classical computer outperforms the
existing classical linear models. Eigen component analysis network (ECAN), a
network of concatenated ECA models, enhances ECA and gains the potential to be
not only integrated with nonlinear models, but also an interface for deep
neural networks to implement on a quantum computer, by analogizing a data set
as recordings of quantum states. Therefore, ECA and ECAN promise to expand the
feasibility of linear learning models, by adopting the strategy of quantum
machine learning to replace heavy nonlinear models with succinct linear
operations in tackling complexity.
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