Robust and Efficient Quantum Communication

Abstract

Quantum communication between quantum processors offers new capabilities and applications in quantum computing. However, Noisy Intermediate-Scale Quantum (NISQ) devices face challenges such as decoherence, entanglement distillation latency, high communication-to-data qubit ratio, quantum error correction, and scalability. Inspired by distributed systems concepts, this paper presents two solutions for optimizing quantum communication: advanced quantum repeaters and machine learning for quantum network optimization. Advanced quantum repeaters will leverage topological quantum states to improve entanglement generation, swapping, and distillation efficiency. Concurrently, machine learning techniques using multi-armed bandit algorithms will dynamically allocate quantum processing resources across distributed quantum networks. This optimization enhances the efficiency of quantum teleportation protocols and reduces computational costs. By integrating advanced quantum repeaters with machine learning optimization, the proposed solutions aim to address the challenges in quantum communication.