Graph isomorphism and adiabatic quantum computing
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Abstract (arXiv)
In the Graph Isomorphism problem two N-vertex graphs G and G' are given and the task is to determine whether there exists a permutation of the vertices of G that preserves adjacency and transforms G into G'. If yes, then G and G' are said to be isomorphic; otherwise they are non-isomorphic. The GI problem is an important problem in computer science and is thought to be of comparable difficulty to integer factorization. In this paper we present a quantum algorithm that solves arbitrary instances of GI and can also determine all automorphisms of a given graph. We show how the GI problem can be converted to a combinatorial optimization problem that can be solved using adiabatic quantum evolution. We numerically simulate the algorithm's quantum dynamics and show that it correctly: (i) distinguishes non-isomorphic graphs; (ii) recognizes isomorphic graphs; and (iii) finds all automorphisms of a given graph G. We then discuss the GI quantum algorithm's experimental implementation, and close by showing how it can be leveraged to give a quantum algorithm that solves arbitrary instances of the NP-Complete Sub-Graph Isomorphism problem.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Apr 2013.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 0; 2025: 2; 2026: 1.
7 papers.
- 2022Group-Invariant Quantum Machine Learningcited by 56
- 2021Quantum Permutation Synchronizationcited by 32
- 2021Adiabatic Quantum Graph Matching with Permutation Matrix Constraintscited by 20
- 2025Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimizationcited by 17
- 2025QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RLcited by 11
- 2022QuAnt: Quantum Annealing with Learnt Couplingscited by 3
- 2026Learning structural balance of graphs from quantum spectral featurescited by 0
- 2026Learning structural balance of graphs from quantum spectral features10 Sep 2026
- 2025QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via Agentic RL1 Oct 2025
- 2025Agent-Q: Fine-Tuning Large Language Models for Quantum Circuit Generation and Optimization15 Apr 2025
- 2022QuAnt: Quantum Annealing with Learnt Couplings13 Oct 2022
- 2022Group-Invariant Quantum Machine Learning4 May 2022
- 2021Adiabatic Quantum Graph Matching with Permutation Matrix Constraints8 Jul 2021
- 2021Quantum Permutation Synchronization19 Jan 2021