Quantum algorithms for the fractional Poisson equation via rational approximation

TL;DR

This paper introduces a quantum algorithm for efficiently solving the fractional Poisson equation using rational approximation and Schr"odingerization, achieving exponential advantage in high dimensions.

Contribution

It develops a novel quantum approach combining rational approximation and Schr"odingerization to solve high-dimensional fractional PDEs with exponential speedup.

Findings

Quantum algorithm achieves dimension-independent complexity.

Method overcomes curse of dimensionality in high-dimensional fractional problems.

Explicit quantum circuits enable practical implementation.

Abstract

This paper presents a quantum algorithm for solving the fractional Poisson equation \((-\Delta)^s u = f\) with \(s \in (0,1)\) on bounded domains. The proposed approach combines rational approximation techniques with quantum linear system solvers to achieve exponential quantum advantage. The rational approximation represents the inverse fractional Laplacian as a weighted sum of standard resolvents, transforming the original nonlocal problem into a collection of shifted integer-order partial differential equations. These equations are consolidated into a single large linear system through a modified right-hand side construction that simplifies the quantum implementation. To enable practical implementation, we develop explicit quantum circuits via the Schr\"odingerization technique, which converts the non-unitary dynamics of the linear system into a higher-dimensional Schr\"odinger-type equation, allowing the use of standard Hamiltonian simulation. The circuit construction leverages the decomposition of shift operators to realize the discrete Laplacian and employs controlled operations to implement the select oracle. Under finite difference discretization, we provide detailed algorithmic procedures utilizing block-encoding techniques for the coefficient matrices. A comprehensive complexity analysis demonstrates that the quantum algorithm achieves a dependence on the inverse mesh size \(h^{-1}\) that is independent of the spatial dimension \(d\), in stark contrast to classical methods which suffer from exponential growth in high dimensions. This establishes an exponential quantum advantage for high-dimensional fractional problems, effectively overcoming the curse of dimensionality that limits classical approaches.