Quantum computers are expected to surpass the computational capabilities of classical
computers during this decade and have transformative impact on numerous industry sectors …

Large machine learning models are revolutionary technologies of artificial intelligence
whose bottlenecks include huge computational expenses, power, and time used both in the …

We study a recently introduced simple method [S. **, N. Liu, and Y. Yu, Quantum simulation
of partial differential equations via Schrödingerisation, arxiv: 2212.13969] for solving …

Nonlinear differential equations model diverse phenomena but are notoriously difficult to
solve. While there has been extensive previous work on efficient quantum algorithms for …

We study a new method-called Schrodingerisation introduced in [**, Liu, Yu, arxiv:
2212.13969]-for solving general linear partial differential equations with quantum simulation …

We present substantially generalized and improved quantum algorithms over prior work for
inhomogeneous linear and nonlinear ordinary differential equations (ODE). Specifically, we …

Simulating fluid dynamics on a quantum computer is intrinsically difficult due to the nonlinear
and non-Hamiltonian nature of the Navier-Stokes equation (NSE). We propose a framework …

We construct quantum algorithms to compute the solution and/or physical observables of
nonlinear ordinary differential equations (ODEs) and nonlinear Hamilton-Jacobi equations …

We present a full quantum algorithm for the lattice Boltzmann method for simulating fluid
flows, the only such algorithm to implement both the streaming and collision substeps as …

Loading functions into quantum computers represents an essential step in several quantum
algorithms, such as quantum partial differential equation solvers. Therefore, the inefficiency …