The Effectiveness of Utilizing a Hybrid Quantum Algorithm on Large Scale Network Routing

Tianjie Hu, Jindi Wu, Qun Li

Quantum networks emerge as fundamental frameworks for addressing various large-scale problems. There are two primary architectures: space-based quantum networks, which deploy satellites with free space channels to interconnect users, and ground-based quantum networks, which utilize optical fibers to interconnect users. In this paper, we explore space-ground integrated quantum networks that incorporate both satellites and optical fibers into the infrastructure. This integrated network features three forms of communication: using only free space links, only ground links, or a hybrid usage of free space and ground links. We formulate the routing problem in space-ground integrated quantum networks as an integer programming and propose two solutions: using linear relaxation or a greedy algorithm. The linear relaxation method allows timely scheduling of additional entanglement purification, whereas the greedy method enables quick scheduling. Simulation results demonstrate their effective balancing between network throughput and communication fidelity.

William J. Munro, Nicoló Lo Piparo, Michael Hanks +1 more

Yuheng Xie, Yuanchen Hao, Yuefeng Lin +6 more

Trusted relay quantum key distribution networks (TR-QKDNs) have emerged as one of the most practical solutions for implementing large-scale QKDNs. The development of efficient routing algorithms is crucial to ensure adaptability to diverse network parameters and topologies. However, current approaches suffer from multiple limitations, including insufficient consideration of key influencing factors, excessive reliance on manually configured parameters, and scalability bottlenecks caused by the exponential complexity of classical algorithms. In this paper, we propose a quantum reinforcement learning-based routing algorithm, named hybrid quantum deep deterministic policy gradient (HQ-DDPG) that integrates a custom-designed quantum neural network (QNN) with the deep deterministic policy gradient (DDPG), to intelligently balance multiple influencing factors and dynamically optimize routing decisions in TR-QKDNs. To reduce computational complexity, we further utilize a quantum single-source shortest path (QSSP) algorithm to compute the optimal routing path. Training results demonstrate that the proposed HQ-DDPG outperforms DDPG in terms of performance metrics, achieving nearly double the training convergence speed while reducing resource requirements by approximately 45 times and exhibiting superior network expressiveness. In typical network topology tests, the proposed algorithm consistently maintains a quantum key delivery ratio above 91.35% under high-load demand, significantly surpassing both DDPG and optimized link state routing (OLSR). Finally, leveraging distributed quantum computing, the proposed QNN enables efficient solutions for large-scale TR-QKDN problems with fewer quantum resources.

Kolachana Vinutna, Dolly Upmandewan, Arpit Giri +4 more

Aaryav Mishra, Sainath Bitragunta, Ashuthosh Bhatia

With exponential growth and associated milestones set in quantum information and quantum computing (QC) technologies, QC is becoming a threat to existing key encryption strategies that leverage asymmetric cryptographic algorithms like RSA (Rivest, Shamir, Adleman) encryption. Since these algorithms form the backbone of Internet communication, it becomes essential to utilize secure quantum methods for key generation and distribution. The quantum key distribution (QKD) networks have since been extensively researched and implemented with various communication protocols, primarily utilizing the Quantum Entanglement and Quantum Key Correction paradigms. Efficient routing is one of the significant problems in classical and hybrid networks. It is important to propose novel hybrid and efficient routing protocols based on modern optimization approaches to design secure, fidelitous, and efficient quantum information networks. We perform this optimization by generating a cost function to implement quantum optimization algorithms, namely the Quantum Approximate Optimization Algorithm (QAOA). We further draw a comparison with the state-of-the-art graph theory-based optimization techniques. The primary objective of this paper is to fabricate a robust quantum communication network and to subsequently analyze the effectiveness of quantum based techniques to carry out network routing and link optimization, generating scope for the utilization of quantum architecture to enhance security in Q KD networks.

Bala Krushna Tripathy, T.R. Sooraj