Demo Data from VLDB 2026 / reviewers in the wild / expert
Jinge Bao
dblp:312/4063
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · unresolved
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 · 44% Algorithms and data structures · 44% Computational complexity · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Algorithms and data structures › recursive algorithms
divide-and-conquer |
0.9 | 1 | 2025 | On the Quantum Time Complexity of Divide and Conquer · ICALP 2025 |
Quantum computing and quantum information
quantum algorithms |
0.9 | 1 | 2025 | On the Quantum Time Complexity of Divide and Conquer · ICALP 2025 |
Computational complexity › query complexity
quantum query complexity |
0.3 | 1 | 2025 | On the Quantum Time Complexity of Divide and Conquer · ICALP 2025 |
Methods — techniques the papers use, named apart from their topics
quantum divide and conquer · 0.9QRAM · 0.9QRAG · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | On the Quantum Time Complexity of Divide and ConquerabstractIn this work, we initiate a systematic study of the time complexity of quantum divide and conquer (QD&C) algorithms for classical problems, and propose a general framework for their analysis. We establish generic conditions under which search and minimization problems with classical divide and conquer algorithms are amenable to quantum speedup, and apply these theorems to various problems involving strings, integers, and geometric objects. These include Longest Distinct Substring, Klee's Coverage, several optimization problems on stock transactions, and k-Increasing Subsequence. For most of these problems our quantum time upper bounds match the quantum query lower bounds, up to polylogarithmic factors. We give a structured framework for describing and classifying a wide variety of QD&C algorithms so that quantum speedups can be more easily identified and applied, and prove general statements on QD&C time complexity covering a range of cases, accounting for the time required for all operations. In particular, we explicitly account for memory access operations in the commonly used QRAM (read-only) and QRAG (read-write) models, which are assumed to take unit time in the query model, and which require careful analysis when involved in recursion. Our generic QD&C theorems have several nice features. 1) To apply them, it suffices to come up with a classical divide and conquer algorithm satisfying the conditions of the theorem. The quantization of the algorithm is then completely handled by the theorem. This can make it easier to find applications which admit a quantum speedup, and contrast with dynamic programming algorithms which can be difficult to quantize due to their highly sequential nature. 2) As these theorems give bounds on time complexity, they can be applied to a greater range of problems than those based on query complexity, e.g., where the best-known quantum algorithms require super-linear time. 3) It can handle minimization problems as well as boolean functions, which allows us to improve on the query complexity result of Childs et al. [Childs et al., 2025] for k-Increasing Subsequence by a logarithmic factor. Jonathan Allcock, Jinge Bao, Aleksandrs Belovs, Troy Lee, Miklos Santha |
ICALP | 2 |