# On the Emerging Potential of Quantum Annealing Hardware for Combinatorial Optimization

arXiv: https://arxiv.org/abs/2210.04291 · HTML: https://mlsanity.com/paper/2210.04291/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2022.

## Built on by (most cited first, 8 of 8)

- [Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms](https://mlsanity.com/paper/2211.15631/) (2022)
- [GCS-Q: Quantum Graph Coalition Structure Generation](https://mlsanity.com/paper/2212.11372/) (2022)
- [Combinatorial Reasoning: Selecting Reasons in Generative AI Pipelines via Combinatorial Optimization](https://mlsanity.com/paper/2407.00071/) (2024)
- [Scalable Quantum-Inspired Optimization through Dynamic Qubit Compression](https://mlsanity.com/paper/2412.18571/) (2024)
- [Comparing Quantum Annealing and Spiking Neuromorphic Computing for Sampling Binary Sparse Coding QUBO Problems](https://mlsanity.com/paper/2405.20525/) (2024)
- [Optimizing Package Delivery with Quantum Annealers: Addressing Time-Windows and Simultaneous Pickup and Delivery](https://mlsanity.com/paper/2504.01560/) (2025)
- [Addressing the Minor-Embedding Problem in Quantum Annealing and Evaluating State-of-the-Art Algorithm Performance](https://mlsanity.com/paper/2504.13376/) (2025)
- [Extractive summarization on a CMOS Ising machine](https://mlsanity.com/paper/2601.11491/) (2026)

## Abstract (arXiv)

Over the past decade, the usefulness of quantum annealing hardware for combinatorial optimization has been the subject of much debate. Thus far, experimental benchmarking studies have indicated that quantum annealing hardware does not provide an irrefutable performance gain over state-of-the-art optimization methods. However, as this hardware continues to evolve, each new iteration brings improved performance and warrants further benchmarking. To that end, this work conducts an optimization performance assessment of D-Wave Systems' most recent Advantage Performance Update computer, which can natively solve sparse unconstrained quadratic optimization problems with over 5,000 binary decision variables and 40,000 quadratic terms. We demonstrate that classes of contrived problems exist where this quantum annealer can provide run time benefits over a collection of established classical solution methods that represent the current state-of-the-art for benchmarking quantum annealing hardware. Although this work does not present strong evidence of an irrefutable performance benefit for this emerging optimization technology, it does exhibit encouraging progress, signaling the potential impacts on practical optimization tasks in the future.