PhD Students

Research Focus

My work focuses on the design and implementation of hybrid classical–quantum models, with an emphasis on continuous-variable quantum neural networks (CVQNNs) for real-world data analysis. I have developed quantum machine learning pipelines that combine classical deep learning architectures with quantum circuits for both image and audio processing tasks. This includes multiclass image classification using hybrid models and audio classification based on CVQNNs operating on mel-spectrogram representations. I am particularly interested in the convergence of quantum computing and machine learning, where emerging quantum techniques offer new possibilities for feature extraction, compact representations, and novel computational frameworks. My research seeks to investigate how quantum models can enhance expressivity and potentially outperform purely classical approaches.