We propose and analyze a quantum switch for GKP-qubit-based all-photonic entanglement distribution networks. Its design is compatible with a recently studied design of a GKP-qubit-based all-photonic quantum repeater that achieves high end-to-end entanglement rates despite realistic finite squeezing in the GKP-qubit preparation and homodyne detection inef-ficiencies. Our main objective is to optimize the allocation of a finite number of multiplexed GKP-qubit-based entanglement resources among different, arbitrary distance client-pair connections enabled by the switch. We achieve this by overcoming the limitations of previous studies by optimizing the bipartite entanglement generation between clients of a switch (or repeater) node even when they are not equally spaced from the switch. We then maximize the switch's total throughput while ensuring the rates are distributed fairly among all client-pair connections. To better illustrate our result, we analyze an exemplary datacenter network where each user aims to connect to the datacenter alone. Together with the quantum repeater, the proposed quantum switch provides a way to realize entanglement distribution-based quantum networks of arbitrary topology.

Quantum computing has rapidly evolved in recent years and has established its supremacy in many application domains. While matter-based qubit platforms such as superconducting qubits have received the most attention so far, there is a rising interest in photonic qubits lately, which show advantages in parallelism, speed, and scalability. Photonic qubits are best served by the paradigm of measurement-based quantum computation (MBQC). To deliver the promise of measurement-based photonic quantum computing (MBPQC), the photon cluster state depth and photon utilization are two of the most important metrics. However, little attention has been paid to optimizing the depth and utilization when mapping quantum circuits to the photon clusters. In this paper, we propose a compiler framework that achieves automatic and dynamic depth and utilization optimizations. Our approach consists of an MBPQC mapping mechanism that maps optimized measurement patterns on a cluster state and a cluster state pruning strategy that removes all possible redundancies without impacting the circuit functions. Experimental results on five quantum benchmark with three different qubit numbers indicate our approach achieves an average of 63.4% cluster depth reduction and 22.8% photon utilization improvements.