P-type silicon strip sensors for the new CMS tracker at HL-LHC W Adam, T Bergauer, E Brondolin, M Dragicevic, M Friedl, R Frühwirth, ... Journal of instrumentation 12 (06), P06018, 2017 | 36 | 2017 |
Quantum reinforcement learning: the maze problem N Dalla Pozza, L Buffoni, S Martina, F Caruso Quantum Machine Intelligence 4 (1), 11, 2022 | 19 | 2022 |
Learning the noise fingerprint of quantum devices S Martina, L Buffoni, S Gherardini, F Caruso Quantum Machine Intelligence 4 (1), 8, 2022 | 19 | 2022 |
Machine learning classification of non-Markovian noise disturbing quantum dynamics S Martina, S Gherardini, F Caruso Physica Scripta, 2023 | 18* | 2023 |
Classification of cancer pathology reports: a large-scale comparative study S Martina, L Ventura, P Frasconi IEEE Journal of Biomedical and Health Informatics 24 (11), 3085-3094, 2020 | 13 | 2020 |
Performance evaluation of Fischer's protocol through steady-state analysis of Markov regenerative processes S Martina, M Paolieri, T Papini, E Vicario 2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation …, 2016 | 13 | 2016 |
Trapping in proton irradiated p+-n-n+ silicon sensors at fluences anticipated at the HL-LHC outer tracker W Adam, T Bergauer, M Dragicevic, M Friedl, R Fruehwirth, M Hoch, ... Journal of Instrumentation 11 (04), P04023, 2016 | 13 | 2016 |
Impact of low-dose electron irradiation on n+ p silicon strip sensors Tracker Group of the CMS Collaboration Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2015 | 12 | 2015 |
Quantum pattern recognition on real quantum processing units S Das, J Zhang, S Martina, D Suter, F Caruso Quantum Machine Intelligence 5 (1), 16, 2023 | 10 | 2023 |
Noise fingerprints in quantum computers: Machine learning software tools S Martina, S Gherardini, L Buffoni, F Caruso Software Impacts 12, 100260, 2022 | 8 | 2022 |
Test beam demonstration of silicon microstrip modules with transverse momentum discrimination for the future CMS tracking detector W Adam, T Bergauer, E Brondolin, M Dragicevic, M Friedl, R Frühwirth, ... Journal of Instrumentation 13 (03), P03003, 2018 | 7 | 2018 |
Deep learning enhanced noise spectroscopy of a spin qubit environment S Martina, S Hernández-Gómez, S Gherardini, F Caruso, N Fabbri Machine Learning: Science and Technology 4 (2), 02LT01, 2023 | 6 | 2023 |
Classification of cancer pathology reports with Deep Learning methods S Martina | 5 | 2020 |
The role of data embedding in equivariant quantum convolutional neural networks S Das, S Martina, F Caruso arXiv preprint arXiv:2312.13250, 2023 | 3 | 2023 |
Quantum‐Noise‐Driven Generative Diffusion Models M Parigi, S Martina, F Caruso Advanced Quantum Technologies, 2300401, 2023 | 3 | 2023 |
Machine Learning based Noise Characterization and Correction on Neutral Atoms NISQ Devices E Canonici, S Martina, R Mengoni, D Ottaviani, F Caruso Advanced Quantum Technologies 7 (1), 2300192, 2024 | 2 | 2024 |
Characterisation of irradiated thin silicon sensors for the CMS phase II pixel upgrade W Adam, T Bergauer, E Brondolin, M Dragicevic, M Friedl, R Frühwirth, ... The European Physical Journal C 77, 1-13, 2017 | 2 | 2017 |
Experimental quantum pattern recognition in IBMQ and diamond NVs S Das, J Zhang, S Martina, D Suter, F Caruso arXiv preprint arXiv:2205.00561, 2022 | 1 | 2022 |
Transfer learning with generative models for object detection on limited datasets M Paiano, S Martina, C Giannelli, F Caruso Machine Learning: Science and Technology, 2024 | | 2024 |
Quantum Reinforcement Learning: the Maze problem ND Pozza, L Buffoni, S Martina, F Caruso arXiv preprint arXiv:2108.04490, 2021 | | 2021 |