The research exposed critical privacy vulnerabilities in Quantum Machine Learning (QML) models, demonstrating that attackers could infer membership of training data with up to 90.2% accuracy in simulations and 75.3% on real quantum hardware via Membership Inference Attacks (MIA). This reveals a systemic risk where sensitive data—such as patterns in datasets like MNIST—could be reverse-engineered, compromising confidentiality. While the team mitigated risks using quantum unlearning techniques (reducing MIA success to near 0% in simulations and 0.9–7.7% on hardware), the initial vulnerability highlights a fundamental flaw in QML’s data protection mechanisms, particularly in high-stakes domains like healthcare or finance where training data may include personally identifiable or proprietary information. The attack vector exploits quantum circuit intermediate outputs (predictions, losses), enabling reconstruction of training data subsets. Though unlearning was effective, the pre-mitigation exposure poses a severe threat to organizations adopting QML without robust privacy safeguards, risking regulatory non-compliance (e.g., GDPR) and intellectual property theft if adversaries exploit these leaks.