Hamiltonian simulation for nonlinear partial differential equation by Schrödingerization

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Authors: Shoya Sasaki, Katsuhiro Endo, Mayu Muramatsu
Cited by: 1
References: 41

Abstract

Hamiltonian simulation is a fundamental algorithm in quantum computing that has attracted considerable interest owing to its potential to efficiently solve the governing equations of large-scale classical systems. Exponential speedup through Hamiltonian simulation has been rigorously demonstrated in the case of coupled harmonic oscillators. The question arises as to whether Hamiltonian simulations in other physical systems also accelerate exponentially. Schrödingerization is a technique that transforms the governing equations of classical systems into the Schrödinger equation. However, since the Schrödinger equation is a linear equation, Hamiltonian simulation is often limited to linear equations. The research on Hamiltonian simulation methods for nonlinear governing equations remains relatively limited. In this study, we propose a Hamiltonian simulation method for nonlinear partial differential equations (PDEs). The proposed method is named Carleman linearization + Schrödingerization (CLS), which combines Carleman linearization (CL) and warped phase transformation (WPT). CL is first applied to transform a nonlinear PDE into a linear differential equation. This linearized equation is then mapped to the Schrödinger equation via WPT. The original nonlinear PDE can be solved efficiently by the Hamiltonian simulation of the resulting Schrödinger equation. By applying this method, we transform the original governing equation into the Schrödinger equation. Solving the transformed Schrödinger equation then enables the analysis of the original nonlinear equation. As a specific application, we apply this method to the nonlinear reaction-diffusion equation to demonstrate that Hamiltonian simulations are applicable to nonlinear PDEs.

Fields of Study & Subjects

Numerical methods for differential equations Model Reduction and Neural Networks Neural Networks and Reservoir Computing

Publication details

Journal / source
Scientific Reports
Publisher
Springer Science and Business Media LLC
Volume
16
Issue
1
Publication date
6 April 2026
Language
EN