Quantum secure direct communication: the quest for machine learning

Quantum communication has developed into various classes, including recent novel quantum secure direct communication (QSDC) schemes. The QSDC method transmits confidential information directly between parties over a quantum channel without requiring a shared private key. Even though QSDC offers improved security guarantees and reduces the possibility of security loopholes, practical applications such as secure information rates, long-distance quantum communication and networking, and integration with existing technologies still need to be improved. Meanwhile, advances in artificial intelligence (AI) and machine learning (ML) continue to revolutionize several fields, including quantum communication, which is challenging to control and maintain effectively for optimum performance. Various ML have been applied to quantum communication systems to mitigate noise-induced errors, analyze communication protocols, and optimize secure key generation rates. While this represents a significant development and has the potential to address challenges facing QSDC applications, we are unaware of ML applications specifically improving QSDC systems. Due to this, we bring forward the discussion and unveil the critical need to integrate AI and ML models into QSDC to enhance security, improve fault tolerance, increase communication distance, and optimize resource allocation. Consequently, this work establishes a practical framework for unlocking the full potential of QSDC protocols and, more broadly, practical quantum communication.