Demo Data from EDBT 2026 / reviewers in the wild / expert
Bob Ricks
dblp:33/3864
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2003
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Quantum computing and quantum information · 100% | |
| Artificial intelligence
1 paper |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Quantum computing and quantum information
quantum machine learning |
0.0 | 1 | 2003 | Training a Quantum Neural Network · NIPS 2003 |
Quantum computing and quantum information › quantum machine learning
quantum neural network |
0.0 | 1 | 2003 | Training a Quantum Neural Network · NIPS 2003 |
Methods — techniques the papers use, named apart from their topics
quantum computing · 0.1gradient-based training · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Training a Quantum Neural NetworkabstractMost proposals for quantum neural networks have skipped over the prob- lem of how to train the networks. The mechanics of quantum computing are different enough from classical computing that the issue of training should be treated in detail. We propose a simple quantum neural network and a training method for it. It can be shown that this algorithm works in quantum systems. Results on several real-world data sets show that this algorithm can train the proposed quantum neural networks, and that it has some advantages over classical learning algorithms. Bob Ricks, Dan Ventura |
NIPS | 1 |