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Detecting Fraudulent Services on Quantum Cloud Platforms via Dynamic Fingerprinting

1 Pith paper cite this work. Polarity classification is still indexing.

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abstract

Noisy Intermediate-Scale Quantum (NISQ) devices, while accessible via cloud platforms, face challenges due to limited availability and suboptimal quality. These challenges raise the risk of cloud providers offering fraudulent services. This emphasizes the need for users to detect such fraud to protect their investments and ensure computational integrity. This study introduces a novel dynamic fingerprinting method for detecting fraudulent service provision on quantum cloud platforms, specifically targeting machine substitution and profile fabrication attacks. The dynamic fingerprint is constructed using a \textit{single} probing circuit to capture the unique error characteristics of quantum devices, making this approach practical because of its trivial computational costs. When the user examines the service, the execution results of the probing circuit act as the device-side fingerprint of the quantum device providing the service. The user then generates the user-side fingerprint by estimating the expected execution result, assuming the correct device is in use. We propose an algorithm for users to construct the user-side fingerprint with linear complexity. By comparing the device-side and user-side fingerprints, users can effectively detect fraudulent services. Our experiments on the IBM Quantum platform, involving seven devices with varying capabilities, confirm the method's effectiveness.

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cs.CR 1

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2024 1

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CONDITIONAL 1

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representative citing papers

Forensics of Transpiled Quantum Circuits

cs.CR · 2024-12-25 · conditional · novelty 5.0

From transpiled quantum circuits, the paper recovers the hardware coupling map and identifies the backend used for transpilation with up to 97.33% accuracy.

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  • Forensics of Transpiled Quantum Circuits cs.CR · 2024-12-25 · conditional · none · ref 3 · internal anchor

    From transpiled quantum circuits, the paper recovers the hardware coupling map and identifies the backend used for transpilation with up to 97.33% accuracy.