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Entanglement Rate Maximization for Dual-Connectivity Wireless Quantum Networks

Kavini Thenuwara, Shiva Kazemi Taskooh, Ekram Hossain

Abstract

The development of quantum networks (QNs) relies on efficient mechanisms for distributing entanglement among multiple quantum users (QUs) under practical system constraints. This paper investigates the problem of entanglement rate maximization in a dual-connectivity (DC) wireless quantum network comprising multiple quantum base stations (QBSs). Under the DC architecture, each QU can associate with up to two QBSs, thereby enhancing resource utilization compared to conventional single-connectivity (SC) schemes. The joint QBS-QU association and entanglement generation rate allocation problem is formulated as a mixed-integer nonlinear programming problem that incorporates practical constraints, including limited QBS entanglement generation capacity as well as heterogeneous minimum entanglement rate demands and fidelity requirements for QUs. To efficiently solve this challenging problem, an alternating optimization (AO) algorithm is developed, which decomposes the original formulation into entanglement rate allocation and association subproblems. Simulation results demonstrate that the proposed DC architecture significantly outperforms SC schemes, while the AO algorithm achieves near-optimal performance with substantially reduced computational complexity.

Entanglement Rate Maximization for Dual-Connectivity Wireless Quantum Networks

Abstract

The development of quantum networks (QNs) relies on efficient mechanisms for distributing entanglement among multiple quantum users (QUs) under practical system constraints. This paper investigates the problem of entanglement rate maximization in a dual-connectivity (DC) wireless quantum network comprising multiple quantum base stations (QBSs). Under the DC architecture, each QU can associate with up to two QBSs, thereby enhancing resource utilization compared to conventional single-connectivity (SC) schemes. The joint QBS-QU association and entanglement generation rate allocation problem is formulated as a mixed-integer nonlinear programming problem that incorporates practical constraints, including limited QBS entanglement generation capacity as well as heterogeneous minimum entanglement rate demands and fidelity requirements for QUs. To efficiently solve this challenging problem, an alternating optimization (AO) algorithm is developed, which decomposes the original formulation into entanglement rate allocation and association subproblems. Simulation results demonstrate that the proposed DC architecture significantly outperforms SC schemes, while the AO algorithm achieves near-optimal performance with substantially reduced computational complexity.

Paper Structure

This paper contains 8 sections, 1 theorem, 10 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model and Assumptions
  3. Network Model
  4. Entanglement Rate
  5. Fidelity
  6. Problem Formulation and solution method
  7. Simulation Results
  8. Conclusion

Key Result

Theorem 1

For a sufficiently large penalty factor $\lambda \gg 1$, problem eq19 is equivalent to the following penalized optimization problem: Here, $\lambda$ acts as a penalty factor that discourages fractional values of $x_{n,j}$, thereby enforcing binary solutions at optimality. $\blacktriangleleft$$\blacktriangleleft$

Figures (5)

  • Figure 1: A schematic view of the considered quantum network.
  • Figure 2: AO convergence for $N=10$ and $U=20$.
  • Figure 3: Total entanglement rate vs. number of QUs when $N=10$.
  • Figure 4: Total entanglement rate vs. number of QBSs when $U=20$.
  • Figure 5: Total entanglement rate vs. different $R_j^{\mathrm{min}}$ ranges when $N=10$ and $U=20$.

Theorems & Definitions (2)

  • Theorem 1
  • proof