Demo Data from VLDB 2026 / reviewers in the wild / expert
Nadezhda Voronova
dblp:342/2870
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0001-9167-9164ORCID · 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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Emerging computing paradigms · 100% | |
| Theoretical computer science
1 paper |
Algorithms and data structures · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Emerging computing paradigms › quantum computing
quantum algorithms |
0.8 | 1 | 2024 | Exponential Quantum Space Advantage for Approximating Maximum Directed Cut in the Streaming Model · STOC 2024 |
Emerging computing paradigms
quantum computing and quantum information |
0.8 | 1 | 2024 | Exponential Quantum Space Advantage for Approximating Maximum Directed Cut in the Streaming Model · STOC 2024 |
Algorithms and data structures › data streams › streaming algorithms
maximum directed cut |
0.8 | 1 | 2024 | Exponential Quantum Space Advantage for Approximating Maximum Directed Cut in the Streaming Model · STOC 2024 |
Algorithms and data structures › data streams
streaming algorithms |
0.8 | 1 | 2024 | Exponential Quantum Space Advantage for Approximating Maximum Directed Cut in the Streaming Model · STOC 2024 |
Methods — techniques the papers use, named apart from their topics
quantum algorithm · 1.5approximation algorithm · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Exponential Quantum Space Advantage for Approximating Maximum Directed Cut in the Streaming ModelabstractWhile the search for quantum advantage typically focuses on speedups in execution time, quantum algorithms also offer the potential for advantage in space complexity. Previous work has shown such advantages for data stream problems, in which elements arrive and must be processed sequentially without random access, but these have been restricted to specially-constructed problems Le Gall, SPAA ‘06 or polynomial advantage Kallaugher, FOCS ‘21. We show an exponential quantum space advantage for the maximum directed cut problem. This is the first known exponential quantum space advantage for any natural streaming problem. This also constitutes the first unconditional exponential quantum resource advantage for approximating a discrete optimization problem in any setting. John Kallaugher, Ojas Parekh, Nadezhda Voronova |
STOC | 3 |