EDBT 2026 Demo / reviewers in the wild / expert
Mengfei Ran
dblp:165/8827
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 · 65% Coding theory · 22% Mathematical optimization · 13% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Coding theory › error-correcting codes › error detection
parity check |
0.8 | 1 | 2024 | Rethinking Parity Check Enhanced Symmetry-Preserving Ansatz · NeurIPS 2024 |
Quantum computing and quantum information
quantum error mitigation |
0.8 | 1 | 2024 | Rethinking Parity Check Enhanced Symmetry-Preserving Ansatz · NeurIPS 2024 |
Quantum computing and quantum information › quantum algorithms
variational quantum algorithms |
0.8 | 1 | 2024 | Rethinking Parity Check Enhanced Symmetry-Preserving Ansatz · NeurIPS 2024 |
Mathematical optimization
combinatorial optimization |
0.2 | 1 | 2024 | Rethinking Parity Check Enhanced Symmetry-Preserving Ansatz · NeurIPS 2024 |
Mathematical optimization › combinatorial optimization › assignment problem
quadratic assignment problem |
0.2 | 1 | 2024 | Rethinking Parity Check Enhanced Symmetry-Preserving Ansatz · NeurIPS 2024 |
Methods — techniques the papers use, named apart from their topics
variational quantum algorithm · 0.8parity check · 0.8hamming weight preserving ansatz · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Rethinking Parity Check Enhanced Symmetry-Preserving AnsatzabstractWith the arrival of the Noisy Intermediate-Scale Quantum (NISQ) era, Variational Quantum Algorithms (VQAs) have emerged to obtain possible quantum advantage. In particular, how to effectively incorporate hard constraints in VQAs remains a critical and open question. In this paper, we manage to combine the Hamming Weight Preserving ansatz with a topological-aware parity check on physical qubits to enforce error mitigation and further hard constraints. We demonstrate the combination significantly outperforms peer VQA methods on both quantum chemistry problems and constrained combinatorial optimization problems e.g. Quadratic Assignment Problem. Intensive experimental results on both simulators and superconducting quantum processors are provided to verify that the combination of HWP ansatz with parity check is among the most promising candidates to demonstrate quantum advantages in the NISQ era to solve more realistic problems. Ge Yan 0001, Mengfei Ran, Ruocheng Wang, Kaisen Pan, Junchi Yan |
NeurIPS | 2 |