Quantum information processing introduces novel approaches for classical data encoding to encompass the complex patterns of input data of practical computational challenges using basic principles of quantum mechanics. The classification of diabetes is an example of a problem that can be efficiently resolved by using quantum unitary operations and the variational quantum classifier (VQC). This study demonstrates the effects of the number of qubits, types of feature maps, optimizers’ class, and the number of layers in the parametrized circuit, and the number of learnable parameters in ansatz influences the effectiveness of the VQC. In total, 76 variants of VQC are analyzed for four and eight qubits’ cases and their results are compared with six classical machine learning models to predict diabetes. Three different types of feature maps (Pauli, Z, and ZZ) are implemented during analysis in addition to three different optimizers (COBYLA, SPSA and SLSQP). Experiments are performed using the PIMA Indian Diabetes Dataset (PIDD). The results conclude that VQC with six layers embedded with an error correction scaling factor of 0.01 and having ZZ feature map and COBYLA optimizer outperforms other quantum variants. The optimal proposed model attained the accuracy of 0.85 and 0.80 for eight and four qubits’ cases, respectively. In addition, the final quantum model among 76 variants was compared with six classical machine learning models. The results suggest that the proposed VQC model has outperformed four classical models including SVM, random forest (RF), decision tree (DT), and linear regression (LR).

In this study, a channel and feature selection methodology is devised for brain‐computer interface (BCI) applications using functional near‐infrared spectroscopy (fNIRS). A graph convolutional network (GCN) is employed to select the appropriate and correlated fNIRS channels. Furthermore, in the feature extraction phase, the performance of two filter‐based feature selection algorithms, (i) the minimum redundancy maximum relevance (mRMR) and (ii) ReliefF, is investigated. The five most commonly used temporal statistical features (i.e., mean, slope, maximum, skewness, and kurtosis) are used, whereas the conventional support vector machine (SVM) is utilized as a classifier for training and testing. The proposed methodology is validated using an available online dataset of motor imagery (left‐ and right‐hand), mental arithmetic, and baseline tasks. First, the efficacy of the proposed methodology is shown for two‐class BCI applications (i.e., left‐ vs. right‐hand motor imagery and mental arithmetic vs. baseline). Second, the proposed framework is applied to four‐class BCI applications (i.e., left‐ vs. right‐hand motor imagery vs. mental arithmetic vs. baseline). The results show that the number of appropriate channels and features was significantly reduced, resulting in a significant increase in classification accuracy for both two‐class and four‐class BCI applications, respectively. Furthermore, both mRMR (i.e., 87.8% for motor imagery, 87.1% for mental arithmetic, and 78.7% for four‐class) and ReliefF (i.e., 90.7% for motor imagery, 93.7% for mental arithmetic, and 81.6% for four‐class) yielded high average classification accuracy (p < 0.05). However, the results of the ReliefF algorithm are more stable and significant.