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DOI: 10.4230/LIPIcs.SEA.2026.27

Cycle Basis Algorithms for Reducing Maximum Edge Participation

Authors: Fan Wang and Sandy Irani

Published in: LIPIcs, Volume 371, 24th International Symposium on Experimental Algorithms (SEA 2026)

Abstract

A cycle basis of a graph is a minimal set of cycles from which every cycle in the graph can be generated by symmetric difference. We study the problem of constructing cycle bases of graphs with low maximum edge participation, defined as the maximum number of cycles in the basis that share any single edge. This quantity, though less studied than total weight or length, plays a critical role in quantum fault tolerance, as it directly impacts the overhead of lattice surgery procedures used to implement an almost universal quantum gate set. Building on a recursive algorithm by Freedman and Hastings, we introduce a family of load-aware heuristics that adaptively select vertices and edges to minimize edge participation throughout the cycle basis construction. Our approach improves empirical performance on random regular graphs and on graphs derived from small quantum codes. We further analyze a simplified balls-into-bins process to establish lower bounds on edge participation. While the model differs from the cycle basis algorithm on real graphs, it captures what can be proven for our heuristics without using more complex graph theoretic properties related to the distribution of cycles in the graph. Our analysis suggests that the maximum load of all of our heuristics will be Ω(log² n). Our results indicate that careful cycle basis construction can yield significant practical benefits in the design of fault-tolerant quantum systems. Maximum edge participation has been studied in the graph theory literature under the name basis number, which is the minimum possible maximum edge participation over all cycle bases in a graph.

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Fan Wang and Sandy Irani. Cycle Basis Algorithms for Reducing Maximum Edge Participation. In 24th International Symposium on Experimental Algorithms (SEA 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 371, pp. 27:1-27:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{wang_et_al:LIPIcs.SEA.2026.27,
  author =	{Wang, Fan and Irani, Sandy},
  title =	{{Cycle Basis Algorithms for Reducing Maximum Edge Participation}},
  booktitle =	{24th International Symposium on Experimental Algorithms (SEA 2026)},
  pages =	{27:1--27:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-422-2},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{371},
  editor =	{Aum\"{u}ller, Martin and Finocchi, Irene},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2026.27},
  URN =		{urn:nbn:de:0030-drops-260311},
  doi =		{10.4230/LIPIcs.SEA.2026.27},
  annote =	{Keywords: Graph algorithms, Cycle Basis, Quantum fault tolerance}
}

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DOI: 10.4230/LIPIcs.SoCG.2022.64

GPU Computation of the Euler Characteristic Curve for Imaging Data

Authors: Fan Wang, Hubert Wagner, and Chao Chen

Published in: LIPIcs, Volume 224, 38th International Symposium on Computational Geometry (SoCG 2022)

Abstract

Persistent homology is perhaps the most popular and useful tool offered by topological data analysis - with point-cloud data being the most common setup. Its older cousin, the Euler characteristic curve (ECC) is less expressive - but far easier to compute. It is particularly suitable for analyzing imaging data, and is commonly used in fields ranging from astrophysics to biomedical image analysis. These fields are embracing GPU computations to handle increasingly large datasets. We therefore propose an optimized GPU implementation of ECC computation for 2D and 3D grayscale images. The goal of this paper is twofold. First, we offer a practical tool, illustrating its performance with thorough experimentation - but also explain its inherent shortcomings. Second, this simple algorithm serves as a perfect backdrop for highlighting basic GPU programming techniques that make our implementation so efficient - and some common pitfalls we avoided. This is intended as a step towards a wider usage of GPU programming in computational geometry and topology software. We find this is particularly important as geometric and topological tools are used in conjunction with modern, GPU-accelerated machine learning frameworks.

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Fan Wang, Hubert Wagner, and Chao Chen. GPU Computation of the Euler Characteristic Curve for Imaging Data. In 38th International Symposium on Computational Geometry (SoCG 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 224, pp. 64:1-64:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{wang_et_al:LIPIcs.SoCG.2022.64,
  author =	{Wang, Fan and Wagner, Hubert and Chen, Chao},
  title =	{{GPU Computation of the Euler Characteristic Curve for Imaging Data}},
  booktitle =	{38th International Symposium on Computational Geometry (SoCG 2022)},
  pages =	{64:1--64:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-227-3},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{224},
  editor =	{Goaoc, Xavier and Kerber, Michael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2022.64},
  URN =		{urn:nbn:de:0030-drops-160724},
  doi =		{10.4230/LIPIcs.SoCG.2022.64},
  annote =	{Keywords: topological data analysis, Euler characteristic, Euler characteristic curve, Betti curve, persistent homology, algorithms, parallel programming, algorithm engineering, GPU programming, imaging data}
}

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Documents authored by Wang, Fan

Cycle Basis Algorithms for Reducing Maximum Edge Participation

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GPU Computation of the Euler Characteristic Curve for Imaging Data

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