In Situ Quantum Analog Pulse Characterization via Structured Signal Processing
This work addresses a critical bottleneck for analog quantum simulators in exploring physical phenomena, offering a lightweight validation protocol to detect hardware faults, though it is incremental as it extends an existing framework.
The paper tackles the problem of calibrating high-fidelity time-dependent pulse control for analog quantum simulators, which existing digital gate methods cannot handle, by presenting a characterization algorithm that reconstructs smooth pulse trajectories directly from propagator queries, achieving high accuracy with efficiency and robustness against errors.
Analog quantum simulators can directly emulate time-dependent Hamiltonian dynamics, enabling the exploration of diverse physical phenomena such as phase transitions, quench dynamics, and non-equilibrium processes. Realizing accurate analog simulations requires high-fidelity time-dependent pulse control, yet existing calibration schemes are tailored to digital gate characterization and cannot be readily extended to learn continuous pulse trajectories. We present a characterization algorithm for in situ learning of pulse trajectories by extending the Quantum Signal Processing (QSP) framework to analyze time-dependent pulses. By combining QSP with a logical-level analog-digital mapping paradigm, our method reconstructs a smooth pulse directly from queries of the time-ordered propagator, without requiring mid-circuit measurements or additional evolution. Unlike conventional Trotterization-based methods, our approach avoids unscalable performance degradation arising from accumulated local truncation errors as the logical-level segmentation increases. Through rigorous theoretical analysis and extensive numerical simulations, we demonstrate that our method achieves high accuracy with strong efficiency and robustness against SPAM as well as depolarizing errors, providing a lightweight and optimal validation protocol for analog quantum simulators capable of detecting major hardware faults.