论文标题
特征组件分析：一种量子理论结合的机器学习技术，以找到线性最大可分离组件
Eigen component analysis: A quantum theory incorporated machine learning technique to find linearly maximum separable components
论文作者
论文摘要
对于线性系统，对刺激的响应通常被其对其他分解刺激的响应所叠加。在量子力学中，状态是多个本征态的叠加。在这里，通过利用相位差异，我们在数据集中确定的一个共同特征，我们提出了特征组件分析（ECA），一个可解释的线性学习模型，该模型将量子力学的原理纳入算法设计的设计中，以提取特征，分类，词典和深度学习，并具有claste clast of ECA，$ simulation $ simulation; $ \ MATHCAL {H} $，在经典计算机上的表现优于现有的经典线性模型。特征组件分析网络（ECAN）是串联ECA模型的网络，增强了ECA并获得了与非线性模型集成的潜力，而且还可以通过将数据集类似于量子计算机上的量子状态来实现深度神经网络在量子计算机上实现的界面。因此，ECA和ECAN承诺通过采用量子机器学习的策略来扩大线性学习模型的可行性，从而用简洁的线性操作替换了解决复杂性的简洁线性操作。
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.
