---
title: "Quantum optimization against the classical state of the…"
description: "Quantum computing, quantum machine learning and quantum annealing appear on 115 recent papers, mostly in AI journals, and 33 of them concern optimization. Firms in finance and logistics run quantum pilots. Do the quantum results beat the best classical solvers on the same instances, or only the classical baselines the papers chose?"
url: https://scholar4ai.com/ideas/quantum-optimization-benchmarks
markdown_url: https://scholar4ai.com/ideas/quantum-optimization-benchmarks.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
date: "2026-10-23"
updated: "2026-10-23"
status: "seed"
tags: ["operations research", "ai", "methods"]
keywords: ["quantum computing", "quantum annealing", "quantum machine learning"]
---

# Quantum optimization against the classical state of the art

Quantum computing, quantum machine learning and quantum annealing appear on 115 recent papers, mostly in AI journals, and 33 of them concern optimization. Firms in finance and logistics run quantum pilots. Do the quantum results beat the best classical solvers on the same instances, or only the classical baselines the papers chose?

## What prompted this

Since October 2025, 115 papers carry a quantum keyword (81 in AI journals, 15 in economics, 13 in operations research): "quantum machine learning" on 22, "quantum computing" on 14, "quantum neural networks" on 9, "hybrid quantum-classical models" on 8. Thirty-three of the 115 also carry an optimization, annealing, combinatorial, portfolio, scheduling, logistics or finance keyword; "quantum annealing" appears on 3 and "quantum optimization" on 2.

## Questions

- On portfolio, routing and scheduling instances used in the quantum papers, how do the reported solutions compare with Gurobi, CPLEX or a tuned metaheuristic given the same time?
- Do the hybrid methods' gains come from the quantum part or from the classical pre- and post-processing?
- At what instance sizes, if any, do current devices beat classical solvers on energy use rather than solution quality?

## Data that could help

The instances and results published in recent quantum optimization papers, and standard classical solvers.

## Next step

Re-solve the benchmark instances of ten recent quantum optimization papers with a commercial solver and a tuned local search under the same time limit.

## Related research activity

*The keywords this note follows, in the tracked finance, accounting, economics, management, operations research and AI journals as of 2026-10-26*

| Keyword | Papers, last 30 days | Rank | Papers per month, Oct 23 – Oct 26* | Papers, last 36 months |
|---|---:|---:|---|---:|
| quantum computing | 1 | #2881 | 0, 0, 0, 0, 0, 0, 1, 0, 3, 1, 2, 1, 1, 3, 3, 3, 3, 4, 2, 2, 0, 1, 0, 1, 1, 1, 3, 3, 0, 0, 0, 0, 3, 2, 2, 2, 1 | 48 |
| quantum annealing | 0 | – | 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 3, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0 | 11 |
| quantum machine learning | 2 | #1303 | 0, 0, 0, 0, 0, 0, 0, 1, 2, 1, 1, 2, 3, 1, 4, 1, 0, 3, 1, 4, 1, 4, 2, 2, 2, 1, 0, 3, 2, 3, 2, 2, 3, 1, 3, 3, 2 | 58 |

## Cite this page

Scholar4AI. (2026). Quantum optimization against the classical state of the art. Journal Research Rankings. Retrieved October 23, 2026, from https://scholar4ai.com/ideas/quantum-optimization-benchmarks

```bibtex
@misc{scholar4ai-ideas-quantum-optimization-benchmarks,
  author = {{Scholar4AI}},
  title = {Quantum optimization against the classical state of the art},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/ideas/quantum-optimization-benchmarks}},
  urldate = {2026-10-23},
  note = {Journal Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).