research
Quantum Queries Learned a Threshold With Logarithmic Calls
The real-domain algorithm used O(log(n/epsilon)) membership queries where classical methods need a linear factor in n.
Summary
The real-domain algorithm used O(log(n/epsilon)) membership queries where classical methods need a linear factor in n.
The paper gives quantum algorithms for learning several linear-threshold-function settings. In the real-domain membership-query model, it uses O(log(n/epsilon)) queries and near-linear additional gates, while its sparse-majority result improves support identification to O(log k) quantum queries under the stated structure.
Why it matters
The real-domain algorithm used O(log(n/epsilon)) membership queries where classical methods need a linear factor in n.
Limits and context
No additional limitation was separately recorded.
Key claims
The real-domain algorithm used O(log(n/epsilon)) membership queries where classical methods need a linear factor in n.
Evidence: source-2026-10-01-020
Sources
- arXiv preprint 2609.40331arXiv · primary research
Corrections
No corrections have been recorded for this story.