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Security Attacks Abusing Pulse-level Quantum Circuits

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arxiv 2406.05941 v2 pith:3DZKP6YD submitted 2024-06-10 cs.CR quant-ph

classification cs.CRquant-ph
keywords quantumcircuitsattackspulse-levelworkmismatchdevelopmentgate
verification ladder T0 review T1 audit T2 compute T3 formal
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This work presents the first thorough exploration of the attacks on the interface between gate-level and pulse-level quantum circuits and pulse-level quantum circuits themselves. Typically, quantum circuits and programs that execute on quantum computers, are defined using gate-level primitives. However, to improve the expressivity of quantum circuits and to allow better optimization, pulse-level circuits are now often used. The attacks presented in this work leverage the inconsistency between the gate-level description of the custom gate, and the actual, low-level pulse implementation of this gate. By manipulating the custom gate specification, this work proposes numerous attacks: qubit plunder, qubit block, qubit reorder, timing mismatch, frequency mismatch, phase mismatch, and waveform mismatch. This work demonstrates these attacks on the real quantum computer and simulator, and shows that most current software development kits are vulnerable to these new types of attacks. In the end, this work proposes a defense framework. The exploration of security and privacy issues of the rising pulse-level quantum circuits provides insight into the future development of secure quantum software development kits and quantum computer systems.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Quantum Properties Trojans (QuPTs) for Attacking Quantum Neural Networks

    quant-ph 2025-07 reject novelty 2.0 of 10

    Quantum Properties Trojans insert extra unitary or Hadamard gates into a QNN circuit, causing accuracy to drop (up to 23% relative) and, for the Hadamard attack, forcing the model to predict a single class.

  2. Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses

    quant-ph 2025-06 conditional novelty 1.0 of 10

    A survey that categorizes known adversarial threats to quantum machine learning systems and reviews existing defenses, from logic locking to hardware-aware watermarking.

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