43 citations · 289 across the 14 of their papers we have counts for
14 papers
Quantum sequential parameter testing
Simon Morelli, Ricard Ravell Rodríguez, John Calsamiglia +3
Sequential strategies in hypothesis testing use a variable number of measurement rounds, allowing a decision to be made as soon as the observed data provide a prescribed level of e…
Comparison of confidence regions for quantum state tomography
Jessica O. de Almeida, Matthias Kleinmann, Gael Sentís
The quantum state associated to an unknown experimental preparation procedure can be determined by performing quantum state tomography. If the statistical uncertainty in the data d…
Universal algorithms for quantum data learning
Marco Fanizza, Michalis Skotiniotis, John Calsamiglia +2
Operating quantum sensors and quantum computers would make data in the form of quantum states available for purely quantum processing, opening new avenues for studying physical pro…
Volumes of parent Hamiltonians for benchmarking quantum simulators
María García Díaz, Gael Sentís, Ramon Muñoz-Tapia +1
We investigate the relative volume of parent Hamiltonians having a target ground state up to some fixed error , a quantity which sets a benchmark on the performance of quantum s…
Quantum Sequential Hypothesis Testing
Esteban Martínez-Vargas, Christoph Hirche, Gael Sentís +4
We introduce sequential analysis in quantum information processing, by focusing on the fundamental task of quantum hypothesis testing. In particular our goal is to discriminate bet…
Bound entangled states fit for robust experimental verification
Gael Sentís, Johannes N. Greiner, Jiangwei Shang +2
Preparing and certifying bound entangled states in the laboratory is an intrinsically hard task, due to both the fact that they typically form narrow regions in the state space, an…
Exact Identification of a Quantum Change Point
Gael Sentís, John Calsamiglia, Ramon Munoz-Tapia
The detection of change points is a pivotal task in statistical analysis. In the quantum realm, it is a new primitive where one aims at identifying the point where a source that su…
Quantifying entanglement of maximal dimension in bipartite mixed states
Gael Sentís, Christopher Eltschka, Otfried Gühne +2
The Schmidt coefficients capture all entanglement properties of a pure bipartite state and therefore determine its usefulness for quantum information processing. While the quantifi…
Inductive supervised quantum learning
Alex Monràs, Gael Sentís, Peter Wittek
In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances. We show that this spl…
Quantum change point
Gael Sentís, Emilio Bagan, John Calsamiglia +2
Sudden changes are ubiquitous in nature. Identifying them is of crucial importance for a number of applications in medicine, biology, geophysics, and social sciences. Here we inves…
Quantum learning of coherent states
Gael Sentís, Madalin Guta, Gerardo Adesso
We develop a quantum learning scheme for binary discrimination of coherent states of light. This is a problem of technological relevance for the reading of information stored in a…
Dealing with ignorance: universal discrimination, learning and quantum correlations
Gael Sentís
The problem of discriminating the state of a quantum system among a number of hypothetical states is usually addressed under the assumption that one has perfect knowledge of the po…
Decomposition of any quantum measurement into extremals
G. Sentís, B. Gendra, S. D. Bartlett +1
We design an efficient and constructive algorithm to decompose any generalized quantum measurement into a convex combination of extremal measurements. We show that if one allows fo…
Multi-copy programmable discrimination of general qubit states
G. Sentís, E. Bagan, J. Calsamiglia +1
Quantum state discrimination is a fundamental primitive in quantum statistics where one has to correctly identify the state of a system that is in one of two possible known states.…