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Ellen Wu

dblp:437/6402 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Quantum computing and quantum information
quantum measurement
1.012026
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
1.012026
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
YearPublicationVenuePosition
2026 The Power of Two Bases: Robust and Copy-Optimal Certification of Nearly All Quantum States with Few-Qubit Measurements
abstract
A 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
STOC4