Quantum k-community detection: algorithm proposals and cross-architectural evaluation

Abstract

Emerging quantum technologies represent a promising alternative for solving hard combinatorial problems in the post-Moore’s law era. For practical purposes, however, the current number of qubits limits the direct applicability to larger real-world instances in the near-term future. Therefore, a promising strategy to overcome this issue is represented by hybrid quantum classical algorithms which leverage classical as well as quantum devices. One prominent example of a hard computational problem is the community detection problem: a partition of a graph into distinct communities such that the ratio between intra-community and inter-community connectivity is maximized. In this paper, we explore the current potential of quantum annealing and gate-based quantum technologies to solve the community detection problem for an arbitrary number of communities. For this purpose, existing algorithms are (re-)implemented and new hybrid algorithms, that can be run on gate-model devices, are proposed. Their performance on standardized benchmark graphs has been evaluated and compared to the one of a state-of-the-art classical heuristic algorithm. Although no quantum speed-up has been achieved, the existing quantum annealing-based methods as well as the novel hybrid algorithms for gate-based quantum computers yield modularity values, which are similar to those of the classical heuristic. However, the modular architecture of the used algorithms allows for fast utilization of more powerful quantum technologies once they become available. Reproducibility: Our code and data are publicly available (Github in Quantum Modularization. https://github.com/jku-win se/quantum_modularization 2021).

Implementation of Quantum Annealing: A Systematic Review

“…There is no benchmark evaluation but this approach was evaluated qualitatively using Twitter data [133] From the aspect of the method used, the findings showed that most of the studies used QA but modifications were proposed to this method and implemented in 2018. This is indicated by the proposed application of the reverse QA method to enhance generalization in neural networks in 2018 [236].…”

“…Table XII also shows the different types of datasets used in 21 studies and none was found to be exactly the same. Some of the methods observed to have been applied in creating and using the datasets include building personal datasets [129,134,172,173,203,208,222,224,232,239], using a representative dataset on a specific problem [133,147,200,204,155,161], and applying several representative datasets [137,139,158,211,236]. The difference in the modes of creating and using these datasets is reasonable due to the variations in the specific issues focused on in each of those studies.…”

Unsupervised Community Detection Algorithm Based on Graph Convolution Network and Social Media

“…It can detect a more meaningful community structure in the network with incomplete information, but the discrimination of invalid information is not high, resulting in the unexpected detection effect. Reference [20] realized community mixing in any number of communities based on quantum annealing and gate quantum technology.…”