Adaptive Policies for Resource Generation in a Quantum Network

Abstract

Protocols for distributed quantum systems commonly require the simultaneous availability of $n$ entangled states, each with a fidelity above some fixed minimum $F_{\mathrm{app}}$ relative to the target maximally-entangled state. However, the fidelity of entangled states degrades over time while in memory. Entangled states are therefore rendered useless when their fidelity falls below $F_{\mathrm{app}}$. This is problematic when entanglement generation is probabilistic and attempted in a sequential manner, because the expected completion time until $n$ entangled states are available can be large. Motivated by existing entanglement generation schemes, we consider a system where the entanglement generation parameters (the success probability $p$ and fidelity $F$ of the generated entangled state) may be adjusted at each time step. We model the system as a Markov decision process, where the policy dictates which generation parameters $(p,F)$ to use for each attempt. We use dynamic programming to derive optimal policies that minimise the expected time until $n$ entangled states are available with fidelity greater than $F_{\mathrm{app}}$. We observe that the advantage of our optimal policies over the selected baselines increases significantly with $n$. In the parameter regimes explored, which are based closely on current experiments, we find that the optimal policy can provide a speed-up of as much as a factor of twenty over a constant-action policy. In addition, we propose a computationally inexpensive heuristic method to compute policies that perform either optimally or near-optimally in the parameter regimes explored. Our heuristic method can be used to find high-performing policies in parameter regimes where finding an optimal policy is intractable.