| A review on quantum approximate optimization algorithm and its variants K Blekos, D Brand, A Ceschini, CH Chou, RH Li, K Pandya, A Summer Physics Reports 1068, 1-66, 2024 | 191 | 2024 |
| Hybrid quantum-classical recurrent neural networks for time series prediction A Ceschini, A Rosato, M Panella 2022 international joint conference on neural networks (IJCNN), 1-8, 2022 | 22 | 2022 |
| Design of an LSTM Cell on a Quantum Hardware A Ceschini, A Rosato, M Panella IEEE Transactions on Circuits and Systems II: Express Briefs 69 (3), 1822-1826, 2021 | 18 | 2021 |
| A fast deep learning technique for Wi-Fi-based human activity recognition F Succetti, A Rosato, F Di Luzio, A Ceschini, M Panella ELECTROMAGNETIC WAVES 174, 127-141, 2022 | 14 | 2022 |
| A General Approach to Dropout in Quantum Neural Networks F Scala, A Ceschini, M Panella, D Gerace Advanced Quantum Technologies, 2300220, 2023 | 12 | 2023 |
| Resource saving via ensemble techniques for quantum neural networks M Incudini, M Grossi, A Ceschini, A Mandarino, M Panella, S Vallecorsa, ... Quantum Machine Intelligence 5 (2), 39, 2023 | 11 | 2023 |
| Deep neural networks for electric energy theft and anomaly detection in the distribution grid A Ceschini, A Rosato, F Succetti, F Di Luzio, M Mitolo, R Araneo, ... 2021 IEEE International Conference on Environment and Electrical Engineering …, 2021 | 9 | 2021 |
| Multivariate prediction of energy time series by autoencoded LSTM networks F Succetti, F Di Luzio, A Ceschini, A Rosato, R Araneo, M Panella 2021 IEEE International Conference on Environment and Electrical Engineering …, 2021 | 6 | 2021 |
| A Review on Quantum Approximate Optimization Algorithm and Its Variants. arXiv 2023 K Blekos, D Brand, A Ceschini, CH Chou, RH Li, K Pandya, A Summer arXiv preprint arXiv:2306.09198, 0 | 6 | |
| Towards Efficient Quantum Hybrid Diffusion Models F De Falco, A Ceschini, A Sebastianelli, BL Saux, M Panella arXiv preprint arXiv:2402.16147, 2024 | 3 | 2024 |
| Modular quantum circuits for secure communication A Ceschini, A Rosato, M Panella IET Quantum Communication 4 (4), 208-217, 2023 | 3 | 2023 |
| Multivariate Time Series Analysis for Electrical Power Theft Detection in the Distribution Grid A Ceschini, A Rosato, F Succetti, R Araneo, M Panella 2022 IEEE International Conference on Environment and Electrical Engineering …, 2022 | 3 | 2022 |
| Time Series Prediction with Autoencoding LSTM Networks F Succetti, A Ceschini, F Di Luzio, A Rosato, M Panella Advances in Computational Intelligence: 16th International Work-Conference …, 2021 | 2 | 2021 |
| A variational approach to quantum gated recurrent units A Ceschini, A Rosato, M Panella Journal of Physics Communications 8 (8), 085004, 2024 | 1 | 2024 |
| Quantum hybrid diffusion models for image synthesis F De Falco, A Ceschini, A Sebastianelli, B Le Saux, M Panella KI-Künstliche Intelligenz, 1-16, 2024 | 1 | 2024 |
| Quantum Simplicial Neural Networks S Piperno, C Battiloro, A Ceschini, F Dominici, P Di Lorenzo, M Panella arXiv preprint arXiv:2501.05558, 2025 | | 2025 |
| New advancements on Quantum Latent Diffusion Models F DE FALCO, A Ceschini, A Sebastianelli, B Le Saux, M Panella Proceedings of International Conference on Quantum Technology for High …, 2025 | | 2025 |
| Machine learning forecast of surface solar irradiance from meteo satellite data A Sebastianelli, F Serva, A Ceschini, Q Paletta, M Panella, B Le Saux Remote Sensing of Environment 315, 114431, 2024 | | 2024 |
| Quantum latent diffusion models F De Falco, A Ceschini, A Sebastianelli, B Le Saux, M Panella Quantum Machine Intelligence 6 (2), 85, 2024 | | 2024 |
| Evolving Hybrid Quantum-Classical GRU Architectures for Multivariate Time Series F De Falco, L Lavagna, A Ceschini, A Rosato, M Panella 2024 IEEE 34th International Workshop on Machine Learning for Signal …, 2024 | | 2024 |
