Entangled links can be seen as a connection between two parties that persists remotely, but whose quality (fidelity) decreases over time. Many quantum applications rely on having a certain number of simultaneously active entangled links for their execution. Every application has a characteristic threshold fidelity - the minimum fidelity a link should have for it to be useful. There are several link generation protocols; a protocol i generates with probability P_i a link with initial fidelity F_i. Until now, research has focused on generating all links with the same protocol. Therefore, the aim of this research is to use multiple generation protocols in order to minimize the expected time needed to reach the desired number of links, and to analyze how this time changes with regard to the threshold fidelity of the target protocol. We present how to reach the minimum possible expected time by modelling the problem as a Markov decision process and determining the optimal sequence of protocols to be used. Then, we observe that our approach generally performs several orders of magnitude better than previous research in which all links were generated with the same protocol.

Many quantum internet applications need access to multiple entangled links existing simultaneously. This requires the generation of multiple entangled links within a time window. Using single entangled link generation protocol, we model this task as a Markov decision process and propose a heuristic-based policy to generate them. This policy chooses different configuration parameters depending on the number of links in the register. Entanglement purification is also incorporated into this heuristic in different ways. To compare, we choose two baseline policies including a previously studied fixed configuration parameter policy. We show that this algorithm completes the generation up to six times faster than the baselines in our simulations and that using entanglement purification can further improve its performance.

Quantum networks allow quantum processors to communicate over large distances. These networks often require simultaneously existing multiple entangled pairs of quantum bits (entangled links) as a fundamental resource for communication. Link generation is a sequential and probabilistic process, and successfully generated links are stored in a quantum memory. Links in memory are subject to noise that causes their quality to decay and become unusable. This paper uses reinforcement learning (RL) to investigate dynamic tuning of the entanglement generation protocol to minimise the time to generate multiple links. By comparing a fixed number of actions to a continuous action space, we analyse the importance of finer-grained tunings of the protocol. This is tested in simulated near-term and medium-term network abstractions. The results show that protocol tuning significantly reduces the mean time to generate entangled links, with finer tuning providing greater benefits up to a point. Furthermore, a heuristic is derived from the RL policies which matches and exceeds their performance. Future work can explore more advanced reinforcement learning algorithms to find better policies, as well as using different noise models to make more generally applicable policies.

A quantum network allows us to connect quantum information processors to achieve capabilities that are not possible using classical computation. Quantum network protocols typically require several entangled states available simultaneously. Previously, an entanglement generation process was analysed where, at each time step, we generate an entangled state with success probability p. Here, we consider adaptive entangled state generation with more flexibility. At each time step, our process chooses a protocol (p_i, F_i) from a discrete number of entanglement generation protocols. An entangled state is generated successfully with probability p_i, and its fidelity F_i defines how close the entangled state is to an ideal Bell state. The new state is subject to depolarising noise in the quantum memory. Because of the memory noise, states are discarded after a certain number of time steps t_i when they are no longer useful to our application. We model our process as a Markov decision process and derive a policy π to generate n entangled states with minimal expected time Eπ[τ]. We analyse the offered improvement of the optimal policy of our adaptive entanglement generation process over the previously studied static process. We conclude that this improvement becomes more significant as the required number of links in memory increases.