## 1 Introduction  
The rise of AI and IoT have revolutionized data analysis, leading to the generation of vast amounts of high-dimensional data across various domains such as business, healthcare, social media, transportation, online education, government, marketing, and finance. However, extracting meaningful insights from this data poses significant challenges for machine learning and data mining models. Traditional data analytics methods often struggle to handle large-scale, high-dimensional datasets, limiting their ability to uncover hidden patterns or knowledge [1]. These datasets frequently include redundant, irrelevant, or noisy features, which increase memory requirements, prolong processing times, and negatively impact performance, ultimately raising processing costs. Dimensionality reduction has become a vital preprocessing technique to address these issues [2]. Its goal is to convert high-dimensional data into a lower-dimensional space while preserving essential information. Two main approaches to dimensionality reduction are Feature Extraction (FE) and Feature Selection (FS). Features refer to measurable characteristicsof the data being analyzed. FE techniques create a new feature space by combining existing features, whereas FS identifies a subset of the original features by removing unnecessary or irrelevant ones based on specific evaluation criteria [3]. While both methods reduce dimensionality, FS is often preferred in domains like text mining and genetic analysis because it retains the interpretability and meaning of the original features. FS methods can be categorized into filter, wrapper, and embedded approaches [4]. Among these, wrapper methods generally provide better accuracy than filter methods, although they require more computational resources [5]. Embedded methods, which integrate feature selection into the training phase of machine learning models, are considered a specialized type of wrapper method [6]. Bio-inspired Optimization Algorithms (BIA) offer innovative solutions inspired by biological evolution and natural behaviors, such as those observed in ant colonies, bird flocks, bee swarms, and fish schools. These algorithms have drawn significant interest in solving complex problems in science and engineering by mimicking the intelligent interactions within biological systems. Evolutionary algorithms, a branch of BIA, solve problems by employing processes that mimic the behaviors of living things. As such, it uses mechanisms that are typically associated with biological evolution, such as reproduction, mutation and recombination [7]. Feature selection is often treated as an optimization problem, where the objective is to identify the most relevant subset of features from the original dataset. Both exhaustive and metaheuristic search techniques are commonly employed to solve these problems [8]. Evolutionary algorithms, which is a metaheuristic method, have shown exceptional effectiveness in tackling wrapper-based feature selection problems [9]. In this paper, we proposed an Adaptive Feature-based Quantum Genetic Algorithm (FbQGA) for feature selection, enhanced by an adaptive mechanism that dynamically adjusts the quantum rotation angle based on the contribution of individual features. This innovation improves the performance and adaptability of the conventional QGA. The proposed FbQGA was benchmarked against state-of-the-art feature selection techniques using five high-dimensional datasets. The results consistently demonstrated that FbQGA outperformed other QGA variants, establishing itself as a highly effective and reliable approach for feature selection. The structure of this paper is as follows: Section 2 provides a detailed overview of related work in the field of quantum bio-inspired algorithms for feature selection. Section 3 describes the theoretical foundation of the quantum genetic algorithm for feature selection. Section 4 shows the design and architecture of our proposed adaptive feature-based quantum genetic algorithm for feature selection.