The realization of the Quantum Internet promises transformative capabilities in secure communication, distributed quantum computing, and high-precision metrology. However, transitioning from laboratory experiments to a scalable, multi-tenant network utility introduces deep orchestration challenges. Current development is often siloed within physics communities, prioritizing hardware, while the classical networking community lacks architectural models to manage fragile quantum resources. This tutorial bridges this divide by providing a network-centric view of quantum networking. We dismantle idealized assumptions in current simulators to address the "simulation-reality gap," recasting them as explicit control-plane constraints. To bridge this gap, we establish Software-Defined Quantum Networking (SDQN) as a prerequisite for scale, prioritizing a symbiotic, dual-plane architecture where classical control dictates quantum data flow. Specifically, we synthesize reference models for SDQN and the Quantum Network Operating System (QNOS) for hardware abstraction, and adapt a Quantum Network Utility Maximization (Q-NUM) framework as a unifying mathematical lens for engineers to reason about trade-offs between entanglement routing, scheduling, and fidelity. Furthermore, we analyze Distributed Quantum AI (DQAI) over imperfect networks as a case study, illustrating how physical constraints such as probabilistic stragglers and decoherence dictate application-layer viability. Ultimately, this tutorial equips network engineers with the tools required to transition quantum networking from a bespoke physics experiment into a programmable, multi-tenant global infrastructure.

翻译：量子互联网的实现有望在安全通信、分布式量子计算和高精度计量等领域带来革命性能力。然而，从实验室实验向可扩展、多租户网络服务的转型，引入了深刻的编排挑战。当前的发展往往局限于物理学界内部，优先关注硬件，而经典网络界缺乏管理脆弱量子资源的架构模型。本教程通过提供以网络为中心的量子网络视角，弥合了这一鸿沟。我们拆解了当前模拟器中的理想化假设，以应对“模拟-现实差距”，并将其转化为明确的控制平面约束。为填补这一差距，我们将软件定义量子网络（SDQN）确立为可扩展性的前提，优先采用一种共生的双平面架构，其中经典控制指导量子数据流。具体而言，我们综合了SDQN的参考模型和用于硬件抽象的量子网络操作系统（QNOS），并适配了量子网络效用最大化（Q-NUM）框架，作为工程师分析纠缠路由、调度与保真度之间权衡的统一数学视角。此外，我们以在不完美网络上的分布式量子人工智能（DQAI）作为案例研究，分析了概率性掉队者和退相干等物理约束如何决定应用层的可行性。最终，本教程为网络工程师提供了必要的工具，将量子网络从定制化的物理实验转变为可编程、多租户的全球基础设施。