Quick Navigation
Topics
Quantum Machine Learning
Quantum Simulation
Quantum State Preparation Representation
How NOT to Fool the Masses When Giving Performance Results for Quantum Computers
arXiv
Authors: Catherine McGeoch
Year
2024
Paper ID
36803
Status
Preprint
Abstract Read
~2 min
Abstract Words
173
Citations
N/A
Abstract
In 1991, David Bailey wrote an article describing techniques for overstating the performance of massively parallel computers. Intended as a lighthearted protest against the practice of inflating benchmark results in order to "fool the masses" and boost sales, the paper sparked development of procedural standards that help benchmarkers avoid methodological errors leading to unjustified and misleading conclusions. Now that quantum computers are starting to realize their potential as viable alternatives to classical computers, we can see the mistakes of three decades ago being repeated by a new batch of researchers who are unfamiliar with this history and these standards. Inspired by Bailey's model, this paper presents four suggestions for newcomers to quantum performance benchmarking, about how not to do it. They are: (1) Don't claim superior performance without mentioning runtimes; (2) Don't report optimized results without mentioning the tuning time needed to optimize those results; (3) Don't claim faster runtimes for (or in comparison to) solvers running on imaginary platforms; and (4) No cherry-picking (without justification and qualification). Suggestions for improving current practice appear in the last section.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2024 reference point for readers tracking recent quantum research.
- In 1991, David Bailey wrote an article describing techniques for overstating the performance of massively parallel computers.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.