Pontryagin Maximum Principle for Rydberg-blockaded state-to-state transfers: A semi-analytic approach
Federico Alberto Astolfi, Sven Jandura, Guido Pupillo
TL;DR
This work develops a semi-analytic approach to time-optimal state-to-state control for ensembles of Rydberg-blockaded qubits by applying the Pontryagin Maximum Principle (PMP) to the block-diagonalized, independent two-level subsystems. For two qubits ($N=2$), abnormal extremals are shown to be either nonexistent or suboptimal, while normal extremals produce a detuning dynamics governed by an ordinary differential equation that mirrors the motion of a classical particle in a quartic potential. The detuning dynamics $rac{1}{2}\\dot{\Delta}^2 + V(\Delta)=0$ with a quartic potential $V(\Delta)$ reduces the time-optimal control to solving an ODE with coefficients determined by conserved quantities, and the resulting pulses agree with GRAPE solutions, providing a robust semi-analytic route to high-fidelity, time-minimal gates. The framework blends analytic PMP structure with numerical optimization to offer both physical insight and practical control prescriptions for multi-qubit operations in Rydberg-based quantum processors.
Abstract
We study time-optimal state-to-state control for two- and multi-qubit operations motivated by neutral-atom quantum processors within the Rydberg blockade regime. Block-diagonalization of the Hamiltonian simplifies the dynamics and enables the application of a semi-analytic approach to the Pontryagin Maximum Principle to derive optimal laser controls. We provide a general formalism for $N$ qubits. For $N=2$ qubits, we classify normal and abnormal extremals, showcasing examples where abnormal solutions are either absent or suboptimal. For normal extremals, we establish a correspondence between the laser detuning from atomic transitions and the motion of a classical particle in a quartic potential, yielding a reduced, semi-analytic formulation of the control problem. Combining PMP-based insights with numerical optimization, our approach bridges analytic and computational methods for high-fidelity, time-optimal control.
