ArticlePloS one2020
Detecting multiple communities using quantum annealing on the D-Wave system.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 82 citations in OpenAlex.
- A primer for quantum computing and its applications to healthcare and biomedical research.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics.Molecular biotechnology · 2024Review
- Quantum annealing-based clustering of single cell RNA-seq data.Briefings in bioinformatics · 2023Article
- Quantum computing reduces systemic risk in financial networks.Scientific reports · 2023Article
- Community detection in brain connectomes with hybrid quantum computing.Scientific reports · 2023Article
- Biology and medicine in the landscape of quantum advantages.Journal of the Royal Society, Interface · 2022Review
- A QUBO formulation for top-τ eigencentrality nodes.PloS one · 2022Article
- Solving large break minimization problems in a mirrored double round-robin tournament using quantum annealing.PloS one · 2022Article
- Quantum inspired community detection for analysis of biodiversity change driven by land-use conversion and climate change.Scientific reports · 2021Article
- QUBO formulations for training machine learning models.Scientific reports · 2021Article
- Reduction of the molecular hamiltonian matrix using quantum community detection.Scientific reports · 2021Article
- Binary matrix factorization on special purpose hardware.PloS one · 2021Article
- Quantum isomer search.PloS one · 2020Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A very important problem in combinatorial optimization is the partitioning of a network into communities of densely connected nodes; where the connectivity between nodes inside a particular community is large compared to the connectivity between nodes belonging to different ones. This problem is known as community detection, and has become very important in various fields of science including chemistry, biology and social sciences. The problem of community detection is a twofold problem that consists of determining the number of communities and, at the same time, finding those communities. This drastically increases the solution space for heuristics to work on, compared to traditional graph partitioning problems. In many of the scientific domains in which graphs are used, there is the need to have the ability to partition a graph into communities with the "highest quality" possible since the presence of even small isolated communities can become crucial to explain a particular phenomenon. We have explored community detection using the power of quantum annealers, and in particular the D-Wave 2X and 2000Q machines. It turns out that the problem of detecting at most two communities naturally fits into the architecture of a quantum annealer with almost no need of reformulation. This paper addresses a systematic study of detecting two or more communities in a network using a quantum annealer.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.