New Approach to Safeguard Quantum Machine Learning Models from Reverse Engineering

Quantum machine learning (QML) is gaining traction, particularly with the advent of Noisy Intermediate-Scale Quantum (NISQ) devices. A recent paper titled "AI-driven Reverse Engineering of QML Models" by Archisman Ghosh and Swaroop Ghosh, submitted to arXiv, addresses significant security concerns arising from the use of third-party quantum cloud services. As demand for quantum resources increases, so do the risks associated with intellectual property (IP) protection. The authors highlight the potential for malicious actors to reverse engineer proprietary quantum IPs, which could lead to unauthorized access and modifications of trained parameters and QML architectures.

The paper critiques existing brute-force methods for reverse engineering QML parameters, which are computationally intensive and inefficient. Instead, the authors propose an innovative autoencoder-based approach that significantly reduces the time required to extract parameters from transpiled QML models. Their experiments with multi-qubit classifiers indicate that reverse engineering can be achieved under specific conditions, with a mean error of approximately 0.1. Notably, the time taken to prepare the dataset and train the model for reverse engineering is about 1,000 seconds, which is an improvement of 100 times over previous methods for similar classifiers.

These findings underscore the urgency for enhanced security measures in quantum computing, particularly as reliance on third-party services grows. The implications of this research are critical for developers and organizations utilizing quantum technologies, as they highlight the need for robust defenses against potential IP theft and unauthorized modifications. The full paper can be accessed at arXiv:2408.16929.