Demo Data from EDBT 2026 / reviewers in the wild / expert
Ellen Wu
dblp:437/6402
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0005-3832-2608ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1 · 1 since 2021
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% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Quantum computing and quantum information
quantum measurement |
1.0 | 1 | 2026 | The Power of Two Bases: Robust and Copy-Optimal Certification of Nearly All Quantum States with Few-Qubit Measurements · STOC 2026 |
Quantum computing and quantum information › quantum state testing
quantum state certification |
1.0 | 1 | 2026 | The Power of Two Bases: Robust and Copy-Optimal Certification of Nearly All Quantum States with Few-Qubit Measurements · STOC 2026 |
Methods — techniques the papers use, named apart from their topics
state certification · 1.0few-qubit measurements · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | The Power of Two Bases: Robust and Copy-Optimal Certification of Nearly All Quantum States with Few-Qubit MeasurementsabstractA central task in quantum information science is state certification: testing whether an unknown state is є1-close to a fixed target state, or є2-far. Recent work has shown that surprisingly simple measurement protocols – comprising only single-qubit measurements – suffice to certify arbitrary n-qubit states. However, these certification protocols are not robust: rather than allowing constant є1, they can only positively certify states within є1=O(1/n) trace distance of the target. In many experimental settings, the appropriate error tolerance is constant as the system size grows, so this lack of robustness renders existing tests inapplicable at scale, no matter how many times the test is repeated. Andrea Coladangelo, Jerry Li 0001, Joseph Slote, Ellen Wu |
STOC | 4 |