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

arXiv: https://arxiv.org/abs/2403.10790 · HTML: https://mlsanity.com/paper/2403.10790/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 16 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [Variational Quantum Circuits for Deep Reinforcement Learning](https://mlsanity.com/paper/1907.00397/) (2019)
- [Entangled Watermarks as a Defense against Model Extraction](https://mlsanity.com/paper/2002.12200/) (2020)
- [Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data](https://mlsanity.com/paper/1806.05476/) (2018)
- [An Alternative Probabilistic Interpretation of the Huber Loss](https://mlsanity.com/paper/1911.02088/) (2019)
- [Quantum Physical Unclonable Functions: Possibilities and Impossibilities](https://mlsanity.com/paper/1910.02126/) (2019)

## Built on by (most cited first, 7 of 7)

- [CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise](https://mlsanity.com/paper/2504.00366/) (2025)
- [STIQ: Safeguarding Training and Inferencing of Quantum Neural Networks from Untrusted Cloud](https://mlsanity.com/paper/2405.18746/) (2024)
- [SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness](https://mlsanity.com/paper/2511.14989/) (2025)
- [Security Concerns in Quantum Machine Learning as a Service](https://mlsanity.com/paper/2408.09562/) (2024)
- [Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security](https://mlsanity.com/paper/2507.08623/) (2025)
- [Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms](https://mlsanity.com/paper/2605.03629/) (2026)
- [Characterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Access](https://mlsanity.com/paper/2609.05702/) (2026)

## 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.