6 papers
Hierarchically discriminating Haar-randomness in quantum states from a black-box device
Xavier Bonet-Monroig, Hao Wang, Adrián Pérez-Salinas
The concept of randomness in quantum computing has been central to constructing benchmarking tools, cryptographic protocols, as well as a proof of beyond-classical computation. Dis…
Universal approximation of continuous functions with minimal quantum circuits
Adrián Pérez-Salinas, Mahtab Yaghubi Rad, Alice Barthe +1
The conventional paradigm of quantum computing is discrete: it utilizes discrete sets of gates to realize bitstring-to-bitstring mappings, some of them arguably intractable for cla…
On the relation between trainability and dequantization of variational quantum learning models
Elies Gil-Fuster, Casper Gyurik, Adrián Pérez-Salinas +1
The quest for successful variational quantum machine learning (QML) relies on the design of suitable parametrized quantum circuits (PQCs), as analogues to neural networks in classi…
Multiple-basis representation of quantum states
Adrián Pérez-Salinas, Patrick Emonts, Jordi Tura +1
Classical simulation of quantum physics is a central approach to investigating physical phenomena. Quantum computers enhance computational capabilities beyond those of classical re…
The role of data-induced randomness in quantum machine learning classification tasks
Berta Casas, Xavier Bonet-Monroig, Adrián Pérez-Salinas
Quantum machine learning (QML) has surged as a prominent area of research with the objective to go beyond the capabilities of classical machine learning models. A critical aspect o…
Gradients and frequency profiles of quantum re-uploading models
Alice Barthe, Adrián Pérez-Salinas
Quantum re-uploading models have been extensively investigated as a form of machine learning within the context of variational quantum algorithms. Their trainability and expressivi…