Curator's Take
This article addresses a critical but often overlooked bottleneck in quantum annealing: the process of mapping logical problems onto the sparse, irregular connectivity of actual quantum hardware. The researchers have created the first standardized benchmark suite for comparing embedding algorithms, revealing that no single approach works best across all problem types - a finding that challenges assumptions in the field and suggests the need for adaptive embedding strategies. The Ember framework's comprehensive evaluation across D-Wave's three hardware generations (Chimera, Pegasus, and Zephyr) provides the quantum community with essential infrastructure for reproducible research in this foundational area. For quantum annealing to reach its potential in solving real-world optimization problems, having reliable tools to evaluate and improve the embedding step is crucial, making this contribution particularly valuable for practitioners working with current quantum annealers.
— Mark Eatherly
Summary
Minor embedding is a required compilation step for quantum annealing, mapping logical problem graphs onto sparse hardware topologies. Despite its central role in determining solution quality, no standardized benchmark exists for comparing embedding algorithms: prior studies use incompatible graph libraries, inconsistent metrics, and non-reproducible experimental setups, making cross-algorithm comparisons unreliable. We present Ember (Embedding Minor Benchmark for Evaluative Reproducibility), an open-source benchmarking framework addressing this gap. Ember provides a standardized algorithm interface with seeded, reproducible execution infrastructure; a diverse graph library of 24,016 instances spanning structured, random, and physics-motivated problem types not previously used in embedding benchmarks; and a unified analysis pipeline supporting all three current D-Wave hardware topologies (Chimera, Pegasus, Zephyr). We evaluate five algorithms across the full library on Chimera and find that no algorithm dominates universally: rankings vary systematically with graph structure, and the best algorithm depends on the family being embedded. We also examine the effects of hardware topology (including Pegasus and Zephyr), qubit error rates, and evaluate a reinforcement-learning approach (CHARME) within a narrower test set. Ember is available at https://github.com/zachmacsmith/ember and is installable via pip install ember-qc.