From the 1 of 6 linked papers with an AI index.
6 papers
Cautious optimism for deep parameterized quantum circuits
Marie Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto +5
A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs). In particular, it remains unclear how their performa…
Quantum memory advantage for quantum process tomography
Carlos Bravo-Prieto, Weiyuan Gong, Antonio Anna Mele
The paper proves that quantum memory enables a lower query complexity for quantum process tomography than any protocol without quantum memory, establishing a provable advantage.
Learning complexity gradually in quantum machine learning models
Erik Recio-Armengol, Franz J. Schreiber, Jens Eisert +1
Quantum machine learning is an emergent field that continues to draw significant interest for its potential to offer improvements over classical algorithms in certain areas. Howeve…
A PAC-Bayesian approach to generalization for quantum models
Pablo Rodriguez-Grasa, Matthias C. Caro, Jens Eisert +3
Generalization is a central concept in machine learning theory, yet for quantum models, it is predominantly analyzed through uniform bounds that depend on a model's overall capacit…
Prospects for quantum advantage in machine learning from the representability of functions
Sergi Masot-Llima, Elies Gil-Fuster, Carlos Bravo-Prieto +2
Demonstrating quantum advantage in machine learning tasks requires navigating a complex landscape of proposed models and algorithms. To bring clarity to this search, we introduce a…
Double descent in quantum kernel methods
Marie Kempkes, Aroosa Ijaz, Elies Gil-Fuster +4
The double descent phenomenon challenges traditional statistical learning theory by revealing scenarios where larger models do not necessarily lead to reduced performance on unseen…