ChainForge：将嵌入表征为量子退火器工作负载的瓶颈
ChainForge: Characterizing Embedding as the Bottleneck in Quantum Annealer Workloads
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中文总结 AI 辅助
本研究提出ChainForge，通过表征嵌入对量子退火器的多方面影响，确立嵌入为其工作负载瓶颈，为相关平台设计提供架构见解。
中文摘要 AI 辅助
量子退火器（QA）是首批实现商用规模的量子计算系统，旨在处理大规模优化问题。与执行编译指令序列的数字系统不同，QA作为模拟单指令机器，直接演化伊辛哈密顿量以获取低能解。要运行应用程序，需先通过嵌入将逻辑问题图映射到硬件的稀疏连接结构，其中逻辑变量由一串相连的物理量子比特表示。因此，嵌入成为QA的核心系统挑战，决定了工作负载能否在机器上运行以及如何运行。尽管嵌入作用关键，但在很大程度上被视为预处理步骤，而非系统瓶颈。本研究提出ChainForge，这是首个针对QA工作负载中嵌入的系统与架构表征工作。通过在现代QA硬件上使用多样化的工作负载族和图拓扑，我们表征了嵌入对有效硬件容量、路由开销、运行时变异性及解质量的影响。结果显示，嵌入会增加物理资源使用，长链会降低退火保真度与可扩展性，且即便存在有效嵌入，启发式嵌入器也可能失败。我们还发现，对于需要频繁重映射的动态工作负载，嵌入延迟会成为运行时瓶颈，而标称量子比特数会显著高估QA针对实际应用的可用容量。总体而言，我们的研究结果确立了嵌入是QA的决定性工作负载瓶颈与系统抽象，为未来硬件拓扑、运行时系统及工作负载感知退火平台提供了架构见解。
英文摘要
Quantum Annealers (QAs) are among the first commercially scaled quantum computing systems designed for large-scale optimization. Unlike digital systems that execute sequences of compiled instructions, QAs operate as analog single-instruction machines that directly evolve an Ising Hamiltonian toward low-energy solutions. To execute an application, the logical problem graph must first be mapped onto the hardware's sparse connectivity via embedding, where logical variables are represented by chains of connected physical qubits. As a result, embedding becomes the dominant system challenge in QAs, shaping whether and how workloads can execute on the machine. Despite its central role, embedding has largely been treated as a preprocessing step rather than a system bottleneck. In this work, we present ChainForge, the first systems and architecture characterization of embedding in QA workloads. Using diverse workload families and graph topologies on modern QA hardware, we characterize how embedding impacts effective hardware capacity, routing overheads, runtime variability, and solution quality. Our results show that embedding inflates physical resource usage, long chains degrade annealing fidelity and scalability, and heuristic embedders may fail even when valid embeddings exist. We further show that embedding latency can become a runtime bottleneck for dynamic workloads requiring frequent remapping, while nominal qubit counts significantly overestimate the usable capacity of QAs for realistic applications. Overall, our findings establish embedding as the defining workload bottleneck and systems abstraction of QAs, providing architectural insights for future hardware topologies, runtime systems, and workload-aware annealing platforms.
