Machine Learning Optimized Quantum Dot Chemistry for Secure 6G Quantum Communication

The rigid security and performance demands of sixth-generation (6G) networks necessitate advanced quantum communication solutions. This study integrates cadmium selenide (CdSe) quantum dots (QDs) as single-photon sources (SPS) for quantum key distribution (QKD), leveraging their size-dependent emission properties to meet telecom wavelength requirements. Using machine learning-driven optimization, we achieved a 40% reduction in optimization time and high prediction accuracy (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> > 0.92) for emission wavelength and quantum yield. These results were validated by simulations, which demonstrated improved photon purity (g<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>(0) < 0.1) and extraction efficiency (65%), with a key generation rate of 20 Mbps and a quantum bit error rate (QBER) below 3%. By addressing challenges such as photon loss and multi-photon emissions through advanced core/shell engineering and photonic integration, this scalable framework paves the way for next-generation secure communication systems, including potential applications in quantum computing and beyond.