Theoretical Analysis and Simulations of Memory-based and All-photonic Quantum Repeaters and Networks

TL;DR

This work tackles the scalability challenge of quantum networks by contrasting memory-based 1G trapped-ion QRs with all-photonic APE QRs through rigorous theory and NetSquid simulations. It develops detailed models for end-to-end entanglement rate and fidelity, explicitly accounting for memory-qubit resources, link losses, and control-plane dynamics, and it normalizes rate by end-node memory qubits to enable fair comparisons. The study demonstrates regime-dependent performance: APE can outperform memory-based QRs at shorter node spacings and with larger repeater graphs, while memory-based schemes remain competitive at long distances; the results support a possible hybrid network architecture. Overall, the paper provides quantitative guidance for QR hardware design, network planning, and control strategies, and offers open-source tooling (QNPack) to advance future quantum-network research.

Abstract

Developing and deploying advanced Quantum Repeater (QR) technologies will be necessary to scale quantum networks to longer distances. Depending on the error mitigation mechanisms adopted to suppress loss and errors, QRs are typically classified into memory-based or all-photonic QRs; and each type of QR may be best suited for a specific type of underlying quantum technology, a particular scale of quantum networks, or a specific regime of operational parameters. We perform theoretical analysis and simulations of quantum repeaters and networks to investigate the relative performance and resource requirements of different quantum network paradigms. Our results will help guide the optimization of quantum hardware and components and shed light on the role of a robust control plane. We present our research findings on theoretical analysis and simulations of memory-based first-generation trapped-ion quantum repeaters and networks, and all-photonic entanglement-based quantum repeaters and networks. We study the relative performance in terms of entanglement generation rate and fidelity, as well as the resource requirements of these two different quantum network paradigms.

Figures (28)

• Figure 1: HEG between remote ions monga2023quant.
• Figure 2: An 1G quantum repeater and network based on trapped $^{40}\textrm{Ca}^+$ ions.
• Figure 3: Entanglement swapping and end-to-end entanglement generation.
• Figure 4: A two-step scheme to execute HEGs in a repeater chain. Parallel HEGs are executed on every other segment. The overall process can be completed in two steps, (1, 3, 5) in the 1st step and (2, 4) in the 2nd step. When any a HEG fails, the overall end-to-end entanglement generation process will restart.
• Figure 5: An example RGS $|G^3\rangle$ with RGS parameters $(m,b_0,b_1)=(3,3,2)$. Each RGS branch consists of a core qubit(light blue) and a leaf qubit(white). Each core qubit is encoded by a 2-level tree.