Stochastic Multipath Routing for High-Throughput Entanglement Distribution in Quantum Repeater Networks

Abstract

Quantum repeater networks distribute entanglement over lossy links while many users share a limited pool of entangled pairs. Most existing routing schemes either always use a single best path or rely on global optimizations that are hard to run in real time. Here we propose and analyze a simple alternative: a stochastic multipath rule in which each entanglement request is sent at random along one of several edge-disjoint repeater paths, with a single parameter that controls the bias between shorter and longer routes. Using a distance-dependent lossy network model with finite per-link capacities and probabilistic entanglement swapping, we develop an analytic description of the resulting end-to-end entanglement rate as a function of this bias and validate it with large-scale numerical simulations. We find that an intermediate bias consistently outperforms both deterministic extremes across distances, traffic patterns, attenuation, swapping noise, and congestion, bringing the rate close to simple capacity upper bounds and making link usage more even across networks. These results identify stochastic multipath routing as a lightweight classical control strategy for boosting performance and scalability in near-term quantum repeater networks.

Figures (9)

• Figure 1: Throughput $T_r$ versus tournament bias $\gamma$ for several request loads $f_r$. Solid circles show simulations (averaged over $T=1000$ windows); dashed curves show the analytical prediction. The other parameters are $r=0.105$, $C_0=5$, $p_{\mathrm{swap}}=0.95$, $\alpha =1$, $N=500$.
• Figure 2: Plot of the optimal tournament bias $\gamma^{*}$ as a function of request loads $f_r$. The other parameters are the same as in Fig. \ref{['Tr_sn_sim']}.
• Figure 3: Heat map of the optimal tournament bias $\gamma^{*}$ as a function of swap success $p_{\mathrm{swap}}$ and attenuation $\alpha$ for various values of $f_r$ while other parameters are the same as in Fig. \ref{['Tr_sn_sim']}.
• Figure 4: Distribution of the optimal tournament bias $\gamma^{*}$ for different $f_r$, corresponding to the heat maps in Fig. \ref{['fig:gam_opt_alph_pswp']}.
• Figure 5: $T_{r}$ distance scaling of the tournament routing policy for different bias values $\gamma$. Symbols show simulation data and solid lines the exponential fits $T_r(L_{SD};\gamma)\propto e^{-\kappa(\gamma)L_{SD}}$. All other parameters are identical to Fig. \ref{['Tr_sn_sim']} with $f_{r} = 20$.