SQUASH: A SWAP-Based Quantum Attack to Sabotage Hybrid Quantum Neural Networks
Rahul Kumar, Wenqi Wei, Ying Mao, Junaid Farooq, Ying Wang, Juntao Chen

TL;DR
This paper introduces SQUASH, a stealthy quantum attack that sabotages hybrid quantum neural networks by inserting SWAP gates, significantly degrading their classification accuracy without needing access to training data.
Contribution
The paper presents a novel circuit-level attack method, SQUASH, that manipulates the quantum circuit structure to disrupt HQNN performance, highlighting a new vulnerability in quantum machine learning.
Findings
Untargeted SWAP attacks reduce accuracy by up to 74.08%.
Targeted SWAP attacks reduce target class accuracy by up to 79.78%.
SQUASH is highly stealthy and does not require data access.
Abstract
We propose a circuit-level attack, SQUASH, a SWAP-Based Quantum Attack to sabotage Hybrid Quantum Neural Networks (HQNNs) for classification tasks. SQUASH is executed by inserting SWAP gate(s) into the variational quantum circuit of the victim HQNN. Unlike conventional noise-based or adversarial input attacks, SQUASH directly manipulates the circuit structure, leading to qubit misalignment and disrupting quantum state evolution. This attack is highly stealthy, as it does not require access to training data or introduce detectable perturbations in input states. Our results demonstrate that SQUASH significantly degrades classification performance, with untargeted SWAP attacks reducing accuracy by up to 74.08\% and targeted SWAP attacks reducing target class accuracy by up to 79.78\%. These findings reveal a critical vulnerability in HQNN implementations, underscoring the need for more…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Physical Unclonable Functions (PUFs) and Hardware Security · Quantum Information and Cryptography
