39 citations · 128 across the 12 of their papers we have counts for
13 papers
Quantum algorithms for the exponentiation of Toeplitz matrices and applications in partial differential equations
Xabier Gutiérrez, Nicola Mariella, Javier González-Conde +2
We present quantum algorithms to approximate the exponential of banded Toeplitz matrices. These matrices have central importance in many PDE related problems, but their quantum imp…
Quantum Hamiltonian Evolution for Coherent Quantum Learning
Ignacio B. Acedo, Javier Gonzalez-Conde, Pablo Rodriguez-Grasa +2
We introduce Coherent Quantum Learning (CQL), a training framework for quantum learning models in which the model parameters are quantum degrees of freedom evolved under a Hamilton…
Stability of nonlinear dissipative systems with applications in fluid dynamics
Javier Gonzalez-Conde, Daniel Isla, Sergiy Zhuk +1
Nonlinear partial differential equations are central to physics, engineering, and finance. Except in a limited number of integrable cases, their solution generally requires numeric…
Pulsed learning for quantum data re-uploading models
Ignacio B. Acedo, Pablo Rodriguez-Grasa, Pablo Garcia-Azorin +1
While Quantum Machine Learning (QML) holds great potential, its practical realization on Noisy Intermediate-Scale Quantum (NISQ) hardware has been hindered by the limitations of va…
Quantum Algorithm for Local-Volatility Option Pricing via the Kolmogorov Equation
Nikita Guseynov, Mikel Sanz, Ángel Rodríguez-Rozas +2
The solution of option-pricing problems may turn out to be computationally demanding due to non-linear and path-dependent payoffs, the high dimensionality arising from multiple und…
Quantum Carleman linearisation efficiency in nonlinear fluid dynamics
Javier Gonzalez-Conde, Dylan Lewis, Sachin S. Bharadwaj +1
Computational fluid dynamics (CFD) is a specialised branch of fluid mechanics that utilises numerical methods and algorithms to solve and analyze fluid-flow problems. One promising…
Cost of Emulating a Small Quantum Annealing Problem in the Circuit-Model
Javier Gonzalez-Conde, Zachary Morrell, Marc Vuffray +2
Demonstrations of quantum advantage for certain sampling problems have generated considerable excitement for quantum computing and have further spurred the development of circuit-m…
Quantum approximated cloning-assisted density matrix exponentiation
Pablo Rodriguez-Grasa, Ruben Ibarrondo, Javier Gonzalez-Conde +3
Classical information loading is an essential task for many processing quantum algorithms, constituting a cornerstone in the field of quantum machine learning. In particular, the e…
Mixed Quantum-Semiclassical Simulation
Javier Gonzalez-Conde, Andrew T. Sornborger
We study the quantum simulation of mixed quantum-semiclassical (MQS) systems, of fundamental interest in many areas of physics, such as molecular scattering and gravitational backr…
Efficient quantum amplitude encoding of polynomial functions
Javier Gonzalez-Conde, Thomas W. Watts, Pablo Rodriguez-Grasa +1
Loading functions into quantum computers represents an essential step in several quantum algorithms, such as quantum partial differential equation solvers. Therefore, the inefficie…
Quantum algorithms for approximate function loading
Gabriel Marin-Sanchez, Javier Gonzalez-Conde, Mikel Sanz
Loading classical data into quantum computers represents an essential stage in many relevant quantum algorithms, especially in the field of quantum machine learning. Therefore, the…
Efficient Hamiltonian Simulation for Solving Option Price Dynamics
Javier Gonzalez-Conde, Ángel Rodríguez-Rozas, Enrique Solano +1
Pricing financial derivatives, in particular European-style options at different time-maturities and strikes, means a relevant problem in finance. The dynamics describing the price…
Towards Prediction of Financial Crashes with a D-Wave Quantum Computer
Yongcheng Ding, Javier Gonzalez-Conde, Lucas Lamata +7
Prediction of financial crashes in a complex financial network is known to be an NP-hard problem, which means that no known algorithm can guarantee to find optimal solutions effici…