Input-Aware Dynamic Backdoor Attack Against Quantum Neural Networks
Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood. Studies have shown that QNNs are vulnerable to backdoor attacks, yet existing quantum backdoors mostly rely on a fixed trigger shared by all poisoned inputs. This fixed-trigger design is a major weakness because many defenses detect or weaken the repeated patterns such triggers leave in data representations. Although input-aware dynamic backdoors have been studied in classical neural networks, transferring them to QNNs is
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 54%mit-han-lab/torchquantum →
- PossiblePossibly related (embedding) · 53%The_Quantum_Alpha/the-quantum-ad-list →
- PossiblePossibly related (embedding) · 53%FareedKhan-dev/agentic-quantum-computing →
- PossiblePossibly related (embedding) · 51%netket/netket →
- PossiblePossibly related (embedding) · 49%timjm25/QuantumAgent →
- LinkedLinked via arxiv author · 85%Junrui Zhang →
“Input-Aware Dynamic Backdoor Attack Against Quantum Neural Networks”
- LinkedLinked via arxiv author · 85%Zemin Chen →
“Input-Aware Dynamic Backdoor Attack Against Quantum Neural Networks”
- LinkedLinked via arxiv author · 85%Lusi Li →
“Input-Aware Dynamic Backdoor Attack Against Quantum Neural Networks”