Satellite-assisted quantum network (SQN) is emerging as a promising solution to overcome the distance limitations of ground-based fiber quantum network (QN). However, each satellite and ground station has a limited number of transmitters and receivers, respectively, and the Entanglement Distribution Rate (EDR) decreases with the distance between satellites and ground stations, highlighting the need for effective resource allocation to serve requests in the SQN. In this paper, we present a novel optimization problem, termed ESOP, which aims to maximize the total EDR in the network while simultaneously considering the resource capacities of both satellites and ground stations, as well as the fidelity requirements of individual requests. To solve ESOP, we propose a (2 + ϵ)-approximation algorithm, AESOP, which combines a greedy approach with a tailored local search. Simulation results show that AESOP achieves up to 64% improvement in total EDR compared to the existing method.

With the enhanced security offered by quantum teleportation, quantum networks (QNs) are gradually gaining significant attention. However, previous research on QNs often exhausts all network resources to serve requests, hence overlooking the scarce resources in QN. This oversight easily leads to resource wastage and requests competing for limited resources. To address the above issues, this paper introduces a new optimization problem named EINS, which minimizes the maximum resource utilization and finds routing paths for each request through certain intermediate nodes. This design cleverly divides the routing path into segments to enhance routing path diversity. To solve the EINS, we design a novel algorithm named GOAL, which provides an$O(\log\vert V\vert)$bound for constraint deviation. It efficiently and equitably distributes requests, ensuring optimal network utilization. Finally, the simulation results manifest that GOAL can outperform the existing methods by up to 99%.

Quantum key distribution (QKD) is currently the only visible technology for secure symmetric key exchange between communicating parties. However, existing quantum photon sources (PSs) in QKD networks for generating keys between nodes are costly but offer limited achievable key rates. Moreover, achievable key rates across links diminish drastically with link distance, underscoring the importance of effectively identifying relay paths and strategically allocating PS resources. To optimize network costs, it is essential to jointly deploy PSs, allocate key rates across links, and determine relay paths based on anticipated traffic. To this end, we formulate a novel optimization problem, termed DAP, which simultaneously considers PS placement, key rate allocation, and relay path identification. Our proposed$O(\log\vert V\vert)$approximation algorithm, ADAP, leverages an advanced linear programming (LP) conversion with tailored rounding techniques. Simulation results manifest that ADAP achieves at least an 83 % reduction in the number of PSs.

Quantum networks (QNs) gradually gain significant attention due to their higher security compared with classical networks. Conventional approaches in QN routing often aggregate multiple requests into a batch before determining their routing paths. However, this approach may overlook the limited lifetime of qubits, resulting in critical decoherence. In this paper, we present a new online entanglement routing architecture with an online optimization problem and propose a novel [1, O(log |V|)]-competitive algorithm supporting online requests with admission control, aiming to maximize the number of admitted requests. Finally, extensive simulation results show that our algorithm can outperform the existing approaches by up to 98%.