An Efficient Decomposition of the Carleman Linearized Burgers' Equation

Abstract: Herein, we present a polylogarithmic decomposition method to load the matrix from the linearized 1-dimensional Burgers' equation onto a quantum computer. First, we use the Carleman linearization method to map the nonlinear Burgers' equation into an infinite linear system of equations, which is subsequently truncated to order $\alpha$. This new finite linear system is then embedded into a larger system of equations with the key property that its matrix can be decomposed into a linear combination of $\mathcal{O}(\log n_t + \alpha^2\log n_x)$ terms for $n_t$ time steps and $n_x$ spatial grid points. While the terms in this linear combination are not unitary, each is implemented with a simple block encoding and the variational quantuam linear solver (VQLS) routine may be used to obtain a solution. Finally, a complexity analysis of the required VQLS circuits shows that the upper bound of the two-qubit gate depth among all of the block encoded matrices is $\mathcal{O}(\alpha(\log n_x)^2)$. This is therefore the first efficient data loading method of a Carleman linearized system.