74 citations · 142 across the 11 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
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…
Analyzing variational quantum landscapes with information content
Adrián Pérez-Salinas, Hao Wang, Xavier Bonet-Monroig
The parameters of the quantum circuit in a variational quantum algorithm induce a landscape that contains the relevant information regarding its optimization hardness. In this work…
Reduce&chop: Shallow circuits for deeper problems
Adrián Pérez-Salinas, Radoica Draškić, Jordi Tura +1
State-of-the-art quantum computers can only reliably execute circuits with limited qubit numbers and computational depth. This severely reduces the scope of algorithms that can be…
Adiabatic quantum algorithm for artificial graphene
Axel Pérez-Obiol, Adrián Pérez-Salinas, Sergio Sánchez-Ramírez +2
We devise a quantum-circuit algorithm to solve the ground state and ground energy of artificial graphene. The algorithm implements a Trotterized adiabatic evolution from a purely t…
Algorithmic Strategies for seizing Quantum Computing
Adrián Pérez-Salinas
Quantum computing is a nascent technology with prospects to have a huge impact in the world. Its current status, however, only counts on small and noisy quantum computers whose per…
Single-qubit universal classifier implemented on an ion-trap quantum device
Tarun Dutta, Adrián Pérez-Salinas, Jasper Phua Sing Cheng +2
Quantum computers can provide solutions to classically intractable problems under specific and adequate conditions. However, current devices have only limited computational resourc…
One qubit as a Universal Approximant
Adrián Pérez-Salinas, David López-Núñez, Artur García-Sáez +2
A single-qubit circuit can approximate any bounded complex function stored in the degrees of freedom defining its quantum gates. The single-qubit approximant presented in this work…