Emerging Industrial Contexts: Synergy of Quantum Machine Learning & Differential Privacy in Network & Devices

Dear Colleagues,

Quantum computing provides the opportunity for many novel directions and use cases. It is appropriate to position security alongside the principle of quantum computing.  Fostering the plausible advantages of quantum  computing in security  and  its interplay with machine learning scenarios could motivate the deployment of such hybrid applications in an industrial context. However, measuring a pure quantum state across distributed devices and networks is an extremely tedious task,  even when training a machine learning model to trace vulnerabilities. Measurement then becomes a more challenging and uncertain task, with the number of unknown quantum states producing an approximate classical description. Numerically, starting from the perspective of quantum mechanics, a state of n entangled particles requires at least 2n complex numbers for specification. Therefore, quantum state measurement becomes unknown among many states and also argues a basic query: How much information is in a Quantum State? This is followed by the subsequent operation of arbitrary bits and their entanglement, which is responsible for making this measurement harder for computational algorithms. To further investigate and exploit the security measure through machine learning and differential privacy in the quantum environment, we also explore the role of noise-free training in quantum states with machine learning and differential privacy. Digital zero-noise extrapolation (dZNE) has emerged as a common approach for quantum error mitigation. The more we mitigate the error in the quantum state, the better the training; thus, with a Quantum Machine Learning Model, a differential privacy algorithm can finally be formulated. The different non-interactive QLDP algorithm that makes, at most, n queries to a QLDP can also be initiated. Hence, this Special Issue could form a collection of niche research regarding the interplay among machine learning, quantum model and differential privacy.

This Special Issue welcomes papers that address the following innovative ideas:

• Quantum privacy of machine learning
• Privacy preserving algorithms with quantum machine learning
• Differential privacy in variational quantum circuit (VQC)
• privacy in IoT systems: analysis through quantum machine learning
• Post quantum and federated learning for enhancement IOT security
• Quantum local differential privacy and quantum statistical query model
• Quantum machine learning protocol for distributed systems.
• Application of quantum reinforcement learning
• Zero-noise extrapolation in quantum machine learning.

Dr. Soumya Banerjee
Prof. Dr. Samia Bouzefrane
Guest Editors

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• quantum computing
• machine learning
• differential privacy
• QML algorithms
• quantum local differential privacy
• IOT system and quantum differential privacy