Scarlett Gauthier

Scarlett Gauthier, Thirupathaiah Vasantam, Gayane Vardoyan

To effectively support the execution of quantum network applications for multiple sets of user-controlled quantum nodes, a quantum network must efficiently allocate shared resources. We study traffic models for a type of quantum network hub called an Entanglement Generation Switch (EGS), a device that allocates resources to enable entanglement generation between nodes in response to user-generated demand. We propose an on-demand resource allocation algorithm, where a demand is either blocked if no resources are available or else results in immediate resource allocation. We model the EGS as an Erlang loss system, with demands corresponding to sessions whose arrival is modelled as a Poisson process. To reflect the operation of a practical quantum switch, our model captures scenarios where a resource is allocated for batches of entanglement generation attempts, possibly interleaved with calibration periods for the quantum network nodes. Calibration periods are necessary to correct against drifts or jumps in the physical parameters of a quantum node that occur on a timescale that is long compared to the duration of an attempt. We then derive a formula for the demand blocking probability under three different traffic scenarios using analytical methods from applied probability and queueing theory. We prove an insensitivity theorem which guarantees that the probability a demand is blocked only depends upon the mean duration of each entanglement generation attempt and calibration period, and is not sensitive to the underlying distributions of attempt and calibration period duration. We provide numerical results to support our analysis. Our work is the first analysis of traffic characteristics at an EGS system and provides a valuable analytic tool for devising performance driven resource allocation algorithms.

Thomas R. Beauchamp, Hana Jirovská, Scarlett Gauthier et al.

We propose an architecture for scheduling network operations enabling the end-to-end generation of entanglement according to user demand. The main challenge solved by this architecture is to allow for the integration of a network schedule with the execution of quantum programs running on processing end nodes in order to realise quantum network applications. A key element of this architecture is the definition of an entanglement packet to meet application requirements on near-term quantum networks where the lifetimes of the qubits stored at the end nodes are limited. Our architecture is fully modular and hardware agnostic, and defines a framework for further research on specific components that can now be developed independently of each other. In order to evaluate our architecture, we realise a proof of concept implementation on a simulated 6-node network in a star topology. We show our architecture facilitates the execution of quantum network applications, and that robust admission control is required to maintain quality of service. Finally, we comment on potential bottlenecks in our architecture and provide suggestions for future improvements.

Scarlett Gauthier, Thomas R. Beauchamp, Stephanie Wehner

A quantum network's purpose is to enable users to execute applications on end nodes. This requires the network to provide the service of creating entangled links between those nodes. Users of mature networks, such as the internet or the telephone network expect accepted service demands to be met reliably. We first define reliability requirements that extend classical computer network concepts to quantum network service delivery. We then introduce Arqon, a suite of control applications designed to deliver reliable service in centrally controlled quantum networks. We demonstrate through both analytic and numerical evaluation that Arqon satisfies all reliability requirements for accepted demands. These evaluations consider static network topologies. We provide a complete Python implementation and perform complexity analysis showing that admission control scales as $O(k^3)$ in the number of incoming demands $k$ and schedule computation scales as ${O(N^3)}$ in the number of accepted demands to schedule $N$.