Effective feature selection is essential for enhancing the performance of artificial intelligence models. It involves identifying feature combinations that optimize a given metric, but this is a challenging task due to the problem's exponential time complexity. In this study, we present an innovative heuristic called Evolutionary Quantum Feature Selection (EQFS) that employs the Quantum Circuit Evolution (QCE) algorithm. Our approach harnesses the unique capabilities of QCE, which utilizes shallow depth circuits to generate sparse probability distributions. Our computational experiments demonstrate that EQFS can identify good feature combinations with quadratic scaling in the number of features. To evaluate EQFS's performance, we counted the number of times a given classical model assesses the cost function for a specific metric, as a function of the number of generations.

翻译：有效特征选择对于提升人工智能模型性能至关重要。它涉及识别能优化特定指标的特征组合，但由于该问题具有指数级时间复杂度而极具挑战性。本研究提出了一种创新启发式方法——演化量子特征选择（EQFS），该方法采用量子电路演化（QCE）算法。我们的方法利用了QCE的独特能力，通过浅层电路生成稀疏概率分布。计算实验表明，EQFS能以特征数量二次缩放的方式识别优质特征组合。为评估EQFS性能，我们统计了给定经典模型针对特定指标评估代价函数的次数（作为迭代代数的函数）。