Nonlinear kinetic plasma simulation is a compelling target for quantum computing: its high-dimensional phase space offers opportunities for substantial quantum memory savings, while nonlinear particle–field coupling and costly observable extraction pose serious obstacles to an end-to-end speedup. In this talk, I will present an end-to-end quantum algorithm for a weakly nonlinear electron–ion plasma model, together with rigorous convergence and complexity guarantees. The construction combines a plasma free-energy transformation that establishes convergence of the Carleman linearization, a hierarchical block-encoding method that exploits the spatial decay of the electric field, and an observable-estimation procedure that uses the nonlinear information encoded in the Carleman history state. For estimating spacetime-averaged kinetic energy, the algorithm achieves exponential memory savings and a superquadratic runtime improvement over a standard Fourier–Hermite spectral solver. The work provides a controlled nonlinear plasma benchmark for assessing the potential for quantum advantage.