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：有效的特征选择对于提高人工智能模型的性能至关重要。 它涉及确定能优化特定度量的特征组合, 但是由于问题的指数时间复杂性, 这是一项具有挑战性的任务。 在本研究中, 我们展示了一种创新的超常性, 叫做进化量子特征选择( EQFS ), 使用量子电路进化( QCE ) 算法。 我们的方法利用了 QCE 的独特能力, QCE 利用浅深度电路生成微小的概率分布。 我们的计算实验表明 EQFS 可以辨别好特征与特征数量的四倍缩缩缩组合。 为了评估 EQFS 的性能, 我们计算了一个古典模型用来评估特定度的成本函数的次数, 作为几代的函数 。</s>