A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs). In particular, it remains unclear how their performance on unseen data c...

Quantum error correction (QEC) is a hot research topic among quantum computing researchers. This is a short analysis of just how popular QEC is.

How AI could destroy mathematics and barely touch it

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 capacity rather than the sp...

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 framework that conn...

In this work, we establish the strongest known lower bounds against QAC$^0$, while allowing its full power of polynomially many ancillae and gates. Our two main results show that: (1) Depth 3 QAC$^0...

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IQPopt is a software package designed to optimize large-scale instantaneous quantum polynomial circuits on classical hardware. By exploiting an efficient classical simulation algorithm for expectation...

The double descent phenomenon challenges traditional statistical learning theory by revealing scenarios where larger models do not necessarily lead to reduced performance on unseen data. While this co...

Convocatòria 2024-30 PDI Serra Húnter Obert Convocatòria 2024-30 Adscripció: Department: Enginyeria Profile: Machine Learning Termini de sol·licituds: 13/01/2025 Data de publicació a la web:...