11 citations · 16 across the 6 of their papers we have counts for
6 papers
Quantum generative modeling for financial time series with temporal correlations
David Dechant, Eliot Schwander, Lucas van Drooge +4
Quantum generative adversarial networks (QGANs) have been investigated as a method for generating synthetic data with the goal of augmenting training data sets for neural networks.…
Mitigating shot noise in local overlapping quantum tomography with semidefinite programming
Zherui Jerry Wang, David Dechant, Yash J. Patel +1
Reduced density matrices (RDMs) are fundamental in quantum information processing, allowing the computation of local observables, such as energy and correlation functions, without…
Error and Resource Estimates of Variational Quantum Algorithms for Solving Differential Equations Based on Runge-Kutta Methods
David Dechant, Liubov Markovich, Vedran Dunjko +1
A focus of recent research in quantum computing has been on developing quantum algorithms for differential equations solving using variational methods on near-term quantum devices.…
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…
Parameterized quantum circuits as universal generative models for continuous multivariate distributions
Alice Barthe, Michele Grossi, Sofia Vallecorsa +2
Parameterized quantum circuits have been extensively used as the basis for machine learning models in regression, classification, and generative tasks. For supervised learning, the…
Approximation and Generalization Capacities of Parametrized Quantum Circuits for Functions in Sobolev Spaces
Alberto Manzano, David Dechant, Jordi Tura +1
Parametrized quantum circuits (PQC) are quantum circuits which consist of both fixed and parametrized gates. In recent approaches to quantum machine learning (QML), PQCs are essent…