mlsanityData to 7 Oct 2026

QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines

arXiv 2403.10790 ↗First posted 16 Mar 2024quant-ph cs.CR cs.LGcited by 6

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Abstract (arXiv)

Variational quantum circuits (VQCs) have become a powerful tool for implementing Quantum Neural Networks (QNNs), addressing a wide range of complex problems. Well-trained VQCs serve as valuable intellectual assets hosted on cloud-based Noisy Intermediate Scale Quantum (NISQ) computers, making them susceptible to malicious VQC stealing attacks. However, traditional model extraction techniques designed for classical machine learning models encounter challenges when applied to NISQ computers due to significant noise in current devices. In this paper, we introduce QuantumLeak, an effective and accurate QNN model extraction technique from cloud-based NISQ machines. Compared to existing classical model stealing techniques, QuantumLeak improves local VQC accuracy by 4.99%~7.35% across diverse datasets and VQC architectures.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 7 of its 31 references that are in the map. First posted on arXiv 16 Mar 2024.

Builds on

7 of 31 references.

References · 7

  1. 2016Practical Black-Box Attacks against Machine Learningcited by 893
  2. 2016Stealing Machine Learning Models via Prediction APIscited by 602
  3. 2019Variational Quantum Circuits for Deep Reinforcement Learningcited by 165
  4. 2020Entangled Watermarks as a Defense against Model Extractioncited by 144
  5. 2018Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Datacited by 69
  6. 2019An Alternative Probabilistic Interpretation of the Huber Losscited by 43
  7. 2019Quantum Physical Unclonable Functions: Possibilities and Impossibilitiescited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 2.

6 papers.

  1. 2024STIQ: Safeguarding Training and Inferencing of Quantum Neural Networks from Untrusted Cloudcited by 2
  2. 2025CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noisecited by 2
  3. 2024Security Concerns in Quantum Machine Learning as a Servicecited by 1
  4. 2025Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Securitycited by 1
  5. 2026Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithmscited by 0
  6. 2026Characterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Accesscited by 0