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Quantum algorithms for computing observables of nonlinear partial differential equations

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arxiv 2202.07834 v1 pith:ONASN4IU submitted 2022-02-16 quant-ph physics.comp-ph

classification quant-phphysics.comp-ph
keywords nonlinearpdesquantumalgorithmsobservablesadvantagecomputingdata
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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    A quantum algorithm estimates Fokker-Planck reaction rates with sublinear-time, polynomial-in-particle-number cost, giving an exponential-in-particle-number separation from the sharpest classical worst-case Langevin bounds.

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