Optimizing Time-Dependent Hamiltonian Schedules with QiliSDK

This work presents a reproducible workflow for optimizing time-dependent Hamiltonian schedules using QiliSDK, an open-source, backend-agnostic Python framework for analog, digital, and hybrid quantum algorithms. The approach uses variational optimization of parametrized control schedules to improve state preparation beyond standard linear annealing, leveraging non-monotonic and near bang–bang profiles to exploit diabatic effects. The methodology follows a closed optimization loop combining quantum time evolution, expectation-value–based cost evaluation, and classical optimization. Results demonstrate fast convergence and robust improvements in excited-state preparation fidelity compared to naïve interpolation strategies. The contribution also highlights the modular architecture of QiliSDK, showcasing unified abstractions for Hamiltonians, schedules, optimizers, and execution backends, with an emphasis on reproducibility and portability across CPU, GPU, and quantum hardware.