<p>This manuscript addresses the automatic identification of exoplanets with the data from the Kepler mission using variational quantum circuits (VQC) implemented with the Qiskit library. The system accuracy was assessed across various configurations, including different numbers of qubits, feature maps, ansatz structures, and training algorithms, providing valuable insights into the performance of VQCs. The results of the VQC-based approach were compared to those obtained using a classical artificial neural network, namely a multilayer perceptron. The findings demonstrate that VQCs are a viable and promising method for processing astronomical data and discovering exoplanets, offering a potential advantage over traditional machine learning techniques.</p>