198 citations · 721 across the 18 of their papers we have counts for
20 papers
Testing quantum Gaussianity with constant sample complexity
Mahtab Yaghubi Rad, Ricard Puig, Saksham Hassanandani +4
Efficiently testing whether a quantum state possesses a given structure is both a fundamental and a practical task in quantum information. Among the most important structured famil…
An Optimal Quantum Linear Systems Algorithm
Carlos Bravo-Prieto, Aram W. Harrow, Robin Kothari
In the quantum linear systems problem (QLSP), we are given query access to a -sparse matrix with condition number , and the ability to prepare a quantum state…
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
Quantum process tomography, the task of learning an unknown quantum channel from black-box access, is a central problem in quantum information. In this setting, protocols with quan…
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…
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…
Understanding quantum machine learning also requires rethinking generalization
Elies Gil-Fuster, Jens Eisert, Carlos Bravo-Prieto
Quantum machine learning models have shown successful generalization performance even when trained with few data. In this work, through systematic randomization experiments, we sho…
Quantum computing for data analysis in high energy physics
Andrea Delgado, Kathleen E. Hamilton, Prasanna Date +22
Some of the biggest achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and o…
Solving systems of Boolean multivariate equations with quantum annealing
Sergi Ramos-Calderer, Carlos Bravo-Prieto, Ruge Lin +4
Polynomial systems over the binary field have important applications, especially in symmetric and asymmetric cryptanalysis, multivariate-based post-quantum cryptography, coding the…
Style-based quantum generative adversarial networks for Monte Carlo events
Carlos Bravo-Prieto, Julien Baglio, Marco Cè +3
We propose and assess an alternative quantum generator architecture in the context of generative adversarial learning for Monte Carlo event generation, used to simulate particle ph…
Variational Quantum Eigensolver for SU() Fermions
Mirko Consiglio, Wayne J. Chetcuti, Carlos Bravo-Prieto +5
Variational quantum algorithms aim at harnessing the power of noisy intermediate-scale quantum computers, by using a classical optimizer to train a parameterized quantum circuit to…
Quantum autoencoders with enhanced data encoding
Carlos Bravo-Prieto
We present the enhanced feature quantum autoencoder, or EF-QAE, a variational quantum algorithm capable of compressing quantum states of different models with higher fidelity. The…
Qibo: a framework for quantum simulation with hardware acceleration
Stavros Efthymiou, Sergi Ramos-Calderer, Carlos Bravo-Prieto +5
We present Qibo, a new open-source software for fast evaluation of quantum circuits and adiabatic evolution which takes full advantage of hardware accelerators. The growing interes…
Measuring the tangle of three-qubit states
Adrián Pérez-Salinas, Diego García-Martín, Carlos Bravo-Prieto +1
We present a quantum circuit that transforms an unknown three-qubit state into its canonical form, up to relative phases, given many copies of the original state. The circuit is ma…
Scaling of variational quantum circuit depth for condensed matter systems
Carlos Bravo-Prieto, Josep Lumbreras-Zarapico, Luca Tagliacozzo +1
We benchmark the accuracy of a variational quantum eigensolver based on a finite-depth quantum circuit encoding ground state of local Hamiltonians. We show that in gapped phases, t…
Quantum unary approach to option pricing
Sergi Ramos-Calderer, Adrián Pérez-Salinas, Diego García-Martín +4
We present a quantum algorithm for European option pricing in finance, where the key idea is to work in the unary representation of the asset value. The algorithm needs novel circu…
Variational Quantum Linear Solver
Carlos Bravo-Prieto, Ryan LaRose, M. Cerezo +3
Previously proposed quantum algorithms for solving linear systems of equations cannot be implemented in the near term due to the required circuit depth. Here, we propose a hybrid q…
Quantum Singular Value Decomposer
Carlos Bravo-Prieto, Diego García-Martín, José I. Latorre
We present a variational quantum circuit that produces the Singular Value Decomposition of a bipartite pure state. The proposed circuit, that we name Quantum Singular Value Decompo…