



@article {3064,
	title = {Efficient quantum algorithm for nonlinear reaction-diffusion equations and energy estimation},
	year = {2022},
	month = {5/2/2022},
	abstract = {<p>Nonlinear differential equations exhibit rich phenomena in many fields but are notoriously challenging to solve. Recently, Liu et al. [1] demonstrated the first efficient quantum algorithm for dissipative quadratic differential equations under the condition R\&lt;1, where R measures the ratio of nonlinearity to dissipation using the l2 norm. Here we develop an efficient quantum algorithm based on [1] for reaction-diffusion equations, a class of nonlinear partial differential equations (PDEs). To achieve this, we improve upon the Carleman linearization approach introduced in [1] to obtain a faster convergence rate under the condition RD\&lt;1, where RD measures the ratio of nonlinearity to dissipation using the l\&infin; norm. Since RD is independent of the number of spatial grid points n while R increases with n, the criterion RD\&lt;1 is significantly milder than R\&lt;1 for high-dimensional systems and can stay convergent under grid refinement for approximating PDEs. As applications of our quantum algorithm we consider the Fisher-KPP and Allen-Cahn equations, which have interpretations in classical physics. In particular, we show how to estimate the mean square kinetic energy in the solution by postprocessing the quantum state that encodes it to extract derivative information.</p>},
	url = {https://arxiv.org/abs/2205.01141},
	author = {Dong An and Di Fang and Stephen Jordan and Jin-Peng Liu and Guang Hao Low and Jiasu Wang}
}
