Elies Gil-Fuster

18 papers A* 1Journal 17

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A PAC-Bayesian approach to generalization for quantum models. CoRR Pablo Rodriguez-Grasa, Matthias C. Caro, Jens Eisert, Elies Gil-Fuster, Franz J. Schreiber, Carlos Bravo-Prieto
2025	J	`jnl`	Double descent in quantum machine learning. CoRR Marie C. Kempkes, Aroosa Ijaz, Elies Gil-Fuster, Carlos Bravo-Prieto, Jakob Spiegelberg, Evert P. L. van Nieuwenburg, Vedran Dunjko
2025	A*	`conf`	On the Relation between Trainability and Dequantization of Variational Quantum Learning Models. ICLR Elies Gil-Fuster, Casper Gyurik, Adrián Pérez-Salinas, Vedran Dunjko
2025	J	`jnl`	Potential and limitations of random Fourier features for dequantizing quantum machine learning. Quantum Ryan Sweke, Erik Recio-Armengol, Sofiène Jerbi, Elies Gil-Fuster, Bryce Fuller, Jens Eisert, Johannes Jakob Meyer
2025	J	`jnl`	Prospects for quantum advantage in machine learning from the representability of functions. CoRR Sergi Masot-Llima, Elies Gil-Fuster, Carlos Bravo-Prieto, Jens Eisert, Tommaso Guaita
2025	J	`jnl`	Quantum computing and artificial intelligence: status and perspectives. CoRR Giovanni Acampora, Andris Ambainis, Natalia Ares, Leonardo Banchi, Pallavi Bhardwaj, Daniele Binosi, G. Andrew D. Briggs, Tommaso Calarco, Vedran Dunjko, Jens Eisert, Olivier Ezratty, Paul Erker, Federico Fedele, Elies Gil-Fuster, Martin Gärttner, Mats Granath, Markus Heyl, Iordanis Kerenidis, Matthias Klusch, Anton Frisk Kockum, Richard Kueng, Mario Krenn, Jörg Lässig, Antonio Macaluso, Sabrina Maniscalco, Florian Marquardt, Kristel Michielsen, Gorka Muñoz-Gil, Daniel Müssig, Hendrik Poulsen Nautrup, Evert P. L. van Nieuwenburg, Roman Orus, Jörg Schmiedmayer, Markus Schmitt, Philipp Slusallek, Filippo Vicentini, Christof Weitenberg, Frank K. Wilhelm
2024	J	`jnl`	Concept learning of parameterized quantum models from limited measurements. CoRR Beng Yee Gan, Po-Wei Huang, Elies Gil-Fuster, Patrick Rebentrost
2024	J	`jnl`	On the expressivity of embedding quantum kernels. Mach. Learn. Sci. Technol. Elies Gil-Fuster, Jens Eisert, Vedran Dunjko
2024	J	`jnl`	On the relation between trainability and dequantization of variational quantum learning models. CoRR Elies Gil-Fuster, Casper Gyurik, Adrián Pérez-Salinas, Vedran Dunjko
2024	J	`jnl`	Opportunities and limitations of explaining quantum machine learning. CoRR Elies Gil-Fuster, Jonas R. Naujoks, Grégoire Montavon, Thomas Wiegand, Wojciech Samek, Jens Eisert
2023	J	`jnl`	On the expressivity of embedding quantum kernels. CoRR Elies Gil-Fuster, Jens Eisert, Vedran Dunjko
2023	J	`jnl`	Potential and limitations of random Fourier features for dequantizing quantum machine learning. CoRR Ryan Sweke, Erik Recio, Sofiène Jerbi, Elies Gil-Fuster, Bryce Fuller, Jens Eisert, Johannes Jakob Meyer
2023	J	`jnl`	Understanding quantum machine learning also requires rethinking generalization. CoRR Elies Gil-Fuster, Jens Eisert, Carlos Bravo-Prieto
2022	J	`jnl`	Exploiting symmetry in variational quantum machine learning. CoRR Johannes Jakob Meyer, Marian Mularski, Elies Gil-Fuster, Antonio Anna Mele, Francesco Arzani, Alissa Wilms, Jens Eisert
2021	J	`jnl`	Encoding-dependent generalization bounds for parametrized quantum circuits. CoRR Matthias C. Caro, Elies Gil-Fuster, Johannes Jakob Meyer, Jens Eisert, Ryan Sweke
2021	J	`jnl`	Encoding-dependent generalization bounds for parametrized quantum circuits. Quantum Matthias C. Caro, Elies Gil-Fuster, Johannes Jakob Meyer, Jens Eisert, Ryan Sweke
2021	J	`jnl`	Training Quantum Embedding Kernels on Near-Term Quantum Computers. CoRR Thomas Hubregtsen, David Wierichs, Elies Gil-Fuster, Peter-Jan H. S. Derks, Paul K. Fährmann, Johannes Jakob Meyer
2020	J	`jnl`	Data re-uploading for a universal quantum classifier. Quantum Adrián Pérez-Salinas, Alba Cervera-Lierta, Elies Gil-Fuster, José I. Latorre

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.