SQUASH: A SWAP-Based Quantum Attack to Sabotage Hybrid Quantum Neural Networks
This reveals a critical vulnerability in HQNN implementations, which is a problem for quantum machine learning security, though it is incremental as it focuses on a specific attack method.
The paper tackles the problem of securing hybrid quantum neural networks (HQNNs) by proposing SQUASH, a circuit-level attack that inserts SWAP gates to sabotage classification tasks, resulting in accuracy reductions of up to 74.08% for untargeted attacks and 79.78% for targeted attacks.
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 resilient architectures against circuit-level adversarial interventions.