论文标题
使用NISQ算法的量子机学习分类优势的预处理视角
A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithms
论文作者
论文摘要
与经典的机器学习方法相比，量子机器学习（QML）尚未广泛证明其优势。到目前为止，只有在特定情况下，某些量子启发的技术已经实现了少量的增量优势，而考虑到中期未来，混合量子计算中的一些实验案例有望有望（不考虑与使用量子经典算法相关的成就，这是纯粹的成就）。当前的量子计算机嘈杂，几乎没有量子的测试，因此很难证明QML方法的当前和潜在量子优势。这项研究表明，在数据预处理步骤中，我们可以通过使用线性判别分析（LDA）来实现量子分类器的更好的经典编码和性能。结果，变异量子算法（VQA）通过LDA技术和表现优于基线经典分类器在平衡准确性方面的性能提高。
Quantum Machine Learning (QML) hasn't yet demonstrated extensively and clearly its advantages compared to the classical machine learning approach. So far, there are only specific cases where some quantum-inspired techniques have achieved small incremental advantages, and a few experimental cases in hybrid quantum computing are promising considering a mid-term future (not taking into account the achievements purely associated with optimization using quantum-classical algorithms). The current quantum computers are noisy and have few qubits to test, making it difficult to demonstrate the current and potential quantum advantage of QML methods. This study shows that we can achieve better classical encoding and performance of quantum classifiers by using Linear Discriminant Analysis (LDA) during the data preprocessing step. As a result, Variational Quantum Algorithm (VQA) shows a gain of performance in balanced accuracy with the LDA technique and outperforms baseline classical classifiers.
