Quantum Computer Fingerprinting using Error Syndromes

2025-06-19

AI Summary

To address the urgent need for hardware authenticity and result integrity verification in cloud quantum computing, this paper proposes a lightweight hardware fingerprinting authentication method based on error syndrome measurements. Unlike conventional approaches, our method directly leverages syndrome metadata—naturally generated during quantum error correction (QEC)—for device identification, eliminating the need for additional quantum operations or challenge-response protocols; thus, QEC overhead is repurposed as an intrinsic security mechanism. By integrating diverse QEC codes (e.g., Surface code, Repetition code), polymorphic input states, quantum circuit compilation strategies, and machine learning classifiers, we achieve high-fidelity device discrimination on five generations of IBM’s real quantum backends. With only 500 measurement samples, classification accuracy reaches 99%. This work establishes a novel, efficient, low-overhead, and deployable paradigm for hardware-layer trust verification in cloud quantum computing.

Technology Category

• Machine Learning: Quantum Machine Learning
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

As quantum computing matures and moves toward broader accessibility through cloud-based platforms, ensuring the authenticity and integrity of quantum computations becomes an urgent concern. In this work, we propose a strategy to leverage the byproducts of quantum error correction (QEC) to verify hardware identity and authenticate quantum computations for ``free'', without introducing any additional quantum computations or measurements. By treating syndrome measurements as a source of metadata, we embed verification seamlessly into standard QEC protocols and eliminate the need for separate challenge-response pairs. We validate our approach using multiple error-correcting codes, quantum states, and circuit compilation strategies on several generations of IBM quantum computers. Our classifiers achieve 99% accuracy with only 500 shots in distinguishing among five backends. Overall, we re-purpose the intrinsic overhead of error correction to be a mechanism for securing quantum computation.

Problem

• Ensuring quantum computation authenticity via error syndromes
• Leveraging quantum error correction for hardware identity verification
• Embedding verification seamlessly into standard QEC protocols

Innovation

• Leverage quantum error correction byproducts for authentication
• Embed verification seamlessly into standard QEC protocols
• Repurpose error correction overhead for security