mlsanityData to 7 Oct 2026

QTrojan: A Circuit Backdoor Against Quantum Neural Networks

arXiv 2302.08090 ↗First posted 16 Feb 2023quant-ph cs.AI cs.CRcited by 4

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Abstract (arXiv)

We propose a circuit-level backdoor attack, textitQTrojan, against Quantum Neural Networks (QNNs) in this paper. QTrojan is implemented by few quantum gates inserted into the variational quantum circuit of the victim QNN. QTrojan is much stealthier than a prior Data-Poisoning-based Backdoor Attack (DPBA), since it does not embed any trigger in the inputs of the victim QNN or require the access to original training datasets. Compared to a DPBA, QTrojan improves the clean data accuracy by 21% and the attack success rate by 19.9%.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 20 references that are in the map. First posted on arXiv 16 Feb 2023.

Builds on

9 of 20 references.

References · 9

  1. 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,052
  2. 2016Quantum Machine Learningcited by 325
  3. 2020Quantum Long Short-Term Memorycited by 118
  4. 2021QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuitscited by 97
  5. 2020Robust data encodings for quantum classifierscited by 91
  6. 2020Quantum circuit architecture search for variational quantum algorithmscited by 81
  7. 2021The Dawn of Quantum Natural Language Processingcited by 60
  8. 2022Adiabatic Quantum Computing for Multi Object Trackingcited by 22
  9. 2022Scalable Quantum Neural Networks for Classificationcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Q-Detection: A Quantum-Classical Hybrid Poisoning Attack Detection Methodcited by 1
  2. 2025Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Securitycited by 1
  3. 2025SQUASH: A SWAP-Based Quantum Attack to Sabotage Hybrid Quantum Neural Networkscited by 0
  4. 2026Input-Aware Dynamic Backdoor Attack Against Quantum Neural Networkscited by 0