Adaptive Policies for Resource Generation in a Quantum Network

• URL: http://arxiv.org/abs/2509.17576v1
• Date: Mon, 22 Sep 2025 11:04:12 GMT
• Title: Adaptive Policies for Resource Generation in a Quantum Network
• Abstract summary: Protocols for distributed quantum systems commonly require simultaneous availability of $n$ entangled states.<n>We derive optimal policies that minimise the expected time until $n$ entangled states are available with fidelity greater than $F_mathrmapp$.
• Score: 0.5332865877117923
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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