Hand gesture recognition is an essential task in the developing of upper limb prosthesis control systems. This task is typically addressed by classifying surface electromyography (sEMG) signals, i.e. electrical signals generated by muscle activity. Unfortunately, hand gesture recognition results in a very challenging task for several reasons such as signal variability, electrical noise, and the high-dimensionality of sEMG signals. In particular, the high-dimensionality of data makes machine learning models computationally expensive. In this scenario, quantum-powered machine learning models such as the Quantum-enhanced Support Vector Machine (QSVM) may help thanks to their inherently ability to work with large spaces due to the principle of quantum superposition. Starting from this consideration, this paper presents, for the first time, the application of QSVM to classify sEMG signals so as to predict hand gestures. As shown by results, QSVM is a suitable approach to face the hand gesture recognition task.