Who Ran My Circuit: Calibration Data-Based Fingerprinting of Quantum Cloud Hardware

Abstract

Cloud-based access to quantum hardware has become the dominant model for executing quantum workloads in the Noisy Intermediate-Scale Quantum (NISQ) era. However, the lack of transparency in multi-tenant quantum cloud platforms raises security and trust concerns, including the inability of users to verify which quantum processor executed their submitted circuits. In this paper, we propose a calibration data-based quantum device fingerprinting framework that enables verification of quantum cloud hardware without requiring circuit execution or additional measurement overhead. Our approach leverages historical calibration data published by the provider and employs a machine-learning classifier XGBoost to learn distinctive device-specific fingerprints. We evaluate the proposed framework on multiple generations of IBM quantum processors in binary and multi-class identification settings, and achieve 99.03% and 82.85% accuracy, respectively. These experimental results demonstrate that the proposed method achieves high device identification accuracy across heterogeneous hardware architectures while remaining robust to missing or evolving calibration data. Unlike prior fingerprinting techniques that rely on executing large numbers of quantum circuits, our static calibration-driven approach significantly reduces cost and runtime overhead, making it well suited for scalable and practical deployment in quantum cloud environments.