In Situ Quantum Analog Pulse Characterization via Structured Signal Processing

2025-12-02

AI Summary

Conventional calibration of time-dependent pulses in analog quantum simulators suffers from low accuracy, while existing digital gate calibration techniques are incompatible with continuous pulse trajectories. Method: This paper proposes an in-situ pulse characterization method that extends the quantum signal processing framework by integrating logical-layer analog–digital mapping with structured signal reconstruction—enabling continuous-time pulse learning without intermediate measurements or auxiliary evolution. Contribution/Results: Unlike Trotter-based decomposition, our approach avoids piecewise approximation errors, yielding scalable and highly robust pulse reconstruction. Theoretical analysis and numerical simulations demonstrate high fidelity under state-preparation-and-measurement (SPAM) and depolarizing noise, intrinsic capability for hardware fault detection, and significantly improved calibration efficiency over state-of-the-art methods.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

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.

Problem

• Characterizes continuous pulse trajectories for analog quantum simulators.
• Reconstructs smooth pulses without mid-circuit measurements or extra evolution.
• Avoids unscalable performance degradation from local truncation errors.

Innovation

• Extends Quantum Signal Processing for pulse characterization
• Reconstructs smooth pulses via analog-digital mapping without mid-circuit measurements
• Avoids unscalable errors from Trotterization, ensuring robustness and efficiency