Adaptive Machine Learning‐Enhanced Teleportation‐Based Quantum Key Distribution

ABSTRACT

Hybrid quantum networks will be the communication backbone of the future quantum internet, linking together heterogeneous discrete‐variable (DV) and continuous‐variable (CV) quantum systems. However, current quantum key distribution protocols assume a static basis and are not designed for dynamically changing hybrid quantum environments. This paper suggests an adaptive machine learning‐assisted teleportation quantum key distribution framework that integrates the following steps into a quantum‐classical architecture: hybrid DV–CV communication, quantum teleportation, adaptive basis selection and neural‐assisted quantum error correction. Optimal measurement basis prediction, by means of a supervised multilayer perceptron (MLP), using six real‐time channel descriptors, allows for intelligent protocol adaptation under heterogeneous channel conditions. The framework proposed was realized in Qiskit Aer and Scikit‐learn and tested with 6000 different realisations of the different channels. The classification accuracy was 99.44% while the average inference latency was only 0.0011 ms per channel sample, making it possible to use the adaptive basis selector in real time. The teleportation fidelity of AMT‐QKD was 0.899 at a representative channel noise level of 10%, with a quantum bit error rate (QBER) of 0.088, a secret key rate of 0.404 and an eavesdropper mutual information of only . They are shown to be more robust, have less information leakage, and be more secure in generating keys under realistic channel impairments when compared to other protocols, including BB84, Enhanced BB84, B92, BBM92, E91 and CV‐QKD. The findings provide a robust framework for the deployment of AMT‐QKD in secure communication in future hybrid quantum networks and quantum internet applications.