High-precision quantum algorithms for partial differential equations

Andrew M. Childs1,2,3, Jin-Peng Liu1,2,4, and Aaron Ostrander1,2,5

1Joint Center for Quantum Information and Computer Science, University of Maryland, MD 20742, USA
2Institute for Advanced Computer Studies, University of Maryland, MD 20742, USA
3Department of Computer Science, University of Maryland, MD 20742, USA
4Department of Mathematics, University of Maryland, MD 20742, USA
5Department of Physics, University of Maryland, MD 20742, USA

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Quantum computers can produce a quantum encoding of the solution of a system of differential equations exponentially faster than a classical algorithm can produce an explicit description. However, while high-precision quantum algorithms for linear ordinary differential equations are well established, the best previous quantum algorithms for linear partial differential equations (PDEs) have complexity $\mathrm{poly}(1/\epsilon)$, where $\epsilon$ is the error tolerance. By developing quantum algorithms based on adaptive-order finite difference methods and spectral methods, we improve the complexity of quantum algorithms for linear PDEs to be $\mathrm{poly}(d, \log(1/\epsilon))$, where $d$ is the spatial dimension. Our algorithms apply high-precision quantum linear system algorithms to systems whose condition numbers and approximation errors we bound. We develop a finite difference algorithm for the Poisson equation and a spectral algorithm for more general second-order elliptic equations.

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