Quantum computer based feature selection in machine learning

IF 2.5 Q3 QUANTUM SCIENCE & TECHNOLOGY IET Quantum Communication Pub Date : 2024-02-05 DOI:10.1049/qtc2.12086
Gerhard Hellstern, Vanessa Dehn, Martin Zaefferer
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Abstract

The problem of selecting an appropriate number of features in supervised learning problems is investigated. Starting with common methods in machine learning, the feature selection task is treated as a quadratic unconstrained optimisation problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. The different results in small problem instances are compared. According to the results of the authors’ study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, the authors compare the convergence behaviour of the QUBO methods via quantum computing with classical stochastic optimisation methods. Due to persisting error rates, the classical stochastic optimisation methods are still superior.

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机器学习中基于量子计算机的特征选择
本文研究了在监督学习问题中选择适当数量特征的问题。从机器学习的常用方法入手,特征选择任务被视为二次无约束优化问题(QUBO),可以用经典数值方法以及量子计算框架来解决。我们对小问题实例中的不同结果进行了比较。根据作者的研究结果,QUBO 方法是否优于其他特征选择方法取决于数据集。在对包含 27 个特征的更大数据集进行扩展时,作者比较了通过量子计算的 QUBO 方法与经典随机优化方法的收敛行为。由于错误率持续存在,经典随机优化方法仍然更胜一筹。
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