Quantum algorithms for computing observables of nonlinear partial differential equations

Fuente: arXiv
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Hauptverfasser: Jin, Shi, Liu, Nana
Format: Preprint
Veröffentlicht: 2022
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author Jin, Shi
Liu, Nana
author_facet Jin, Shi
Liu, Nana
contents We construct quantum algorithms to compute physical observables of nonlinear PDEs with M initial data. Based on an exact mapping between nonlinear and linear PDEs using the level set method, these new quantum algorithms for nonlinear Hamilton-Jacobi and scalar hyperbolic PDEs can be performed with a computational cost that is independent of M, for arbitrary nonlinearity. Depending on the details of the initial data, it can also display up to exponential advantage in both the dimension of the PDE and the error in computing its observables. For general nonlinear PDEs, quantum advantage with respect to M is possible in the large M limit.
format Preprint
id arxiv_https___arxiv_org_abs_2202_07834
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Quantum algorithms for computing observables of nonlinear partial differential equations
Jin, Shi
Liu, Nana
Quantum Physics
Computational Physics
We construct quantum algorithms to compute physical observables of nonlinear PDEs with M initial data. Based on an exact mapping between nonlinear and linear PDEs using the level set method, these new quantum algorithms for nonlinear Hamilton-Jacobi and scalar hyperbolic PDEs can be performed with a computational cost that is independent of M, for arbitrary nonlinearity. Depending on the details of the initial data, it can also display up to exponential advantage in both the dimension of the PDE and the error in computing its observables. For general nonlinear PDEs, quantum advantage with respect to M is possible in the large M limit.
title Quantum algorithms for computing observables of nonlinear partial differential equations
topic Quantum Physics
Computational Physics
url https://arxiv.org/abs/2202.07834