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This paper presents a significant advancement in quantum algorithms for solving elliptic partial differential equations by introducing a multilevel correction method that mitigates the readout costs associated with spatial derivatives. By coherently canceling fine-coarse discretization errors before measurement, the proposed approach achieves polylogarithmic dependence on mesh refinement, addressing a fundamental bottleneck in current quantum PDE solvers. The work provides explicit operator constructions for finite element and spectral hierarchies, offering a more efficient pathway to estimating physical observables with high accuracy.
Toward Efficient End-to-End Quantum Elliptic PDE Solvers: a Multilevel Correction Algorithm for Direct Observable Estimation
arXiv:2606.01270v2 Announce Type: replace Abstract: Spatial derivatives pose a basic challenge for quantum algorithms for partial differential equations: their standard discretizations have operator norms that grow as the mesh is refined, leading to large block-encoding normalization factors. Even when preconditioning improves access to the solution, estimating derivative-dependent physical observables can reintroduce polynomial dependence on the inverse mesh width. We develop a multilevel quantum algorithm for estimating linear and quadratic observables of elliptic equations that addresses this readout cost. The algorithm decomposes the output into corrections across nested discretizations and realizes fine--coarse cancellation coherently, before measurement. A Ritz-complement factorization connects the $O(h^2)$ size of the corrected response with a block encoding at the same scale, allowing this decay to offset growing readout normalizations. Under efficient quantum access to the corrected coordinates, input data, and readouts, observables with readout scale $O(h^{-\chi})$, $0\le\chi\le2$, can be estimated to additive accuracy $\epsilon$ at cost $\widetilde O(\epsilon^{-1})$ using amplitude estimation or $\widetilde O(\epsilon^{-2})$ using direct sampling, with only polylogarithmic dependence on the inverse finest mesh width $h_L^{-1}$. We give explicit constructions of the required operator oracles for one-dimensional piecewise linear finite elements with diffusion piecewise constant on a fixed coarsest partition, and for Fourier spectral hierarchies. Output-specific bias estimates connect the discrete results to continuum accuracy.
Abstract:Spatial derivatives pose a basic challenge for quantum algorithms for partial differential equations: their standard discretizations have operator norms that grow as the mesh is refined, leading to large block-encoding normalization factors. Even when preconditioning improves access to the solution, estimating derivative-dependent physical observables can reintroduce polynomial dependence on the inverse mesh width. We develop a multilevel quantum algorithm for estimating linear and quadratic observables of elliptic equations that addresses this readout cost. The algorithm decomposes the output into corrections across nested discretizations and realizes fine--coarse cancellation coherently, before measurement. A Ritz-complement factorization connects the $O(h^2)$ size of the corrected response with a block encoding at the same scale, allowing this decay to offset growing readout normalizations. Under efficient quantum access to the corrected coordinates, input data, and readouts, observables with readout scale $O(h^{-\chi})$, $0\le\chi\le2$, can be estimated to additive accuracy $\epsilon$ at cost $\widetilde O(\epsilon^{-1})$ using amplitude estimation or $\widetilde O(\epsilon^{-2})$ using direct sampling, with only polylogarithmic dependence on the inverse finest mesh width $h_L^{-1}$. We give explicit constructions of the required operator oracles for one-dimensional piecewise linear finite elements with diffusion piecewise constant on a fixed coarsest partition, and for Fourier spectral hierarchies. Output-specific bias estimates connect the discrete results to continuum accuracy.
arXiv:2606.01270 [quant-ph]
(or arXiv:2606.01270v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2606.01270 Focus to learn more arXiv-issued DOI via DataCite
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