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Improved Sublinear Algorithms for Classical and Quantum Graph Coloring

Asaf Ferber, Liam Hardiman, Xiaonan Chen

TL;DR

The paper studies sublinear quantum and classical algorithms for coloring graphs with maximum degree $\Delta$, focusing on reducing query complexity in the adjacency- and neighborhood-query models. It introduces a classical Las Vegas algorithm coloring with $(\Delta+1)$ colors using $O(n^2\log n/\Delta)$ adjacency queries, and combines it with a greedy step to reach $O(n^{3/2}\sqrt{\log n})$ queries in expectation. In the quantum setting, it leverages Grover search to obtain sublinear quantum colorings, proving an $O(n^{3/2}\log n/\sqrt{\Delta})$ adjacency-query bound and a $\tilde{O}(n^{4/3})$ quantum-query bound for the same goal, effectively beating classical lower bounds. It also provides near-optimal quantum approaches for $(1+\varepsilon)\Delta$-coloring with two variants: a $O(\varepsilon^{-2} n \log^2 n \sqrt{\Delta})$ neighborhood-query algorithm (success prob. $\ge 2/3$) and a $\tilde{O}(\varepsilon^{-1} n^{5/4})$-query algorithm, both offering polynomial improvements over prior work. Together, these results advance sublinear colorings in both classical and quantum regimes and open avenues for improved partitioning and cross-model methods.

Abstract

We present three sublinear randomized algorithms for vertex-coloring of graphs with maximum degree $Δ$. The first is a simple algorithm that extends the idea of Morris and Song to color graphs with maximum degree $Δ$ using $Δ+1$ colors. Combined with the greedy algorithm, it achieves an expected runtime of $O(n^{3/2}\sqrt{\log n})$ in the query model, improving on Assadi, Chen, and Khanna's algorithm by a $\sqrt{\log n}$ factor in expectation. When we allow quantum queries to the graph, we can accelerate the first algorithm using Grover's famous algorithm, resulting in a runtime of $\tilde{O}(n^{4/3})$ quantum queries. Finally, we introduce a quantum algorithm for $(1+ε)Δ$-coloring, achieving $O(ε^{-1}n^{5/4}\log^{3/2}n)$ quantum queries, offering a polynomial improvement over the previous best bound by Morris and Song.

Improved Sublinear Algorithms for Classical and Quantum Graph Coloring

TL;DR

The paper studies sublinear quantum and classical algorithms for coloring graphs with maximum degree , focusing on reducing query complexity in the adjacency- and neighborhood-query models. It introduces a classical Las Vegas algorithm coloring with colors using adjacency queries, and combines it with a greedy step to reach queries in expectation. In the quantum setting, it leverages Grover search to obtain sublinear quantum colorings, proving an adjacency-query bound and a quantum-query bound for the same goal, effectively beating classical lower bounds. It also provides near-optimal quantum approaches for -coloring with two variants: a neighborhood-query algorithm (success prob. ) and a -query algorithm, both offering polynomial improvements over prior work. Together, these results advance sublinear colorings in both classical and quantum regimes and open avenues for improved partitioning and cross-model methods.

Abstract

We present three sublinear randomized algorithms for vertex-coloring of graphs with maximum degree . The first is a simple algorithm that extends the idea of Morris and Song to color graphs with maximum degree using colors. Combined with the greedy algorithm, it achieves an expected runtime of in the query model, improving on Assadi, Chen, and Khanna's algorithm by a factor in expectation. When we allow quantum queries to the graph, we can accelerate the first algorithm using Grover's famous algorithm, resulting in a runtime of quantum queries. Finally, we introduce a quantum algorithm for -coloring, achieving quantum queries, offering a polynomial improvement over the previous best bound by Morris and Song.

Paper Structure

This paper contains 6 sections, 9 theorems, 33 equations, 1 table, 4 algorithms.

Key Result

Theorem 1

There is a randomized algorithm that, given a graph $G$ of maximum degree $\Delta$, properly $(\Delta+1)$-colors $G$ using $O(n^{2}\log n/\Delta)$ adjacency queries in expectation.

Theorems & Definitions (14)

  • Theorem 1
  • Corollary 2
  • Theorem 3
  • Corollary 4
  • Theorem 5
  • Corollary 6
  • proof : Proof of Theorem \ref{['thm: classical Delta+1']}
  • Theorem 7: Grover's Algorithm boyer1998tight
  • proof : Proof of Theorem \ref{['thm: quantum adjacency']}
  • Lemma 8
  • ...and 4 more