Quantum Computing
Quantum computing aims to leverage quantum mechanics to solve problems intractable for classical computers, primarily focusing on optimization and machine learning. Current research heavily emphasizes the development and application of quantum machine learning algorithms, including variational quantum circuits, quantum neural networks, and quantum kernel methods, often integrated with classical techniques in hybrid approaches. This field holds significant potential for accelerating scientific discovery and impacting various applications, from drug discovery and materials science to financial modeling and medical diagnostics, although challenges in hardware limitations and algorithm design remain.
Papers
Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers
Maurizio Ferrari Dacrema, Fabio Moroni, Riccardo Nembrini, Nicola Ferro, Guglielmo Faggioli, Paolo Cremonesi
Quantum neural network autoencoder and classifier applied to an industrial case study
Stefano Mangini, Alessia Marruzzo, Marco Piantanida, Dario Gerace, Daniele Bajoni, Chiara Macchiavello