Related papers: Solving graph problems using permutation-invariant quantum machine learning

Solving graph problems using permutation-invariant quantum machine learning

URL: http://arxiv.org/abs/2505.12764v2
Date: Tue, 20 May 2025 09:11:28 GMT
Title: Solving graph problems using permutation-invariant quantum machine learning
Authors: Maximilian Balthasar Mansky, Tobias Rohe, Gerhard Stenzel, Alejandro Bravo de la Serna, Santiago Londoño Castillo, Gautham Sathish, Dimitra Nikolaidou, Dmytro Bondarenko, Linus Menzel, Claudia Linnhoff-Popien,
Abstract summary: In quantum machine learning, the ansatz can be tuned to correspond to the specific symmetry of the problem.<n>We show how the symmetry can be included in the quantum circuit in a straightforward constructive method.
Score: 35.99391901074448
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Many computational problems are unchanged under some symmetry operation. In classical machine learning, this can be reflected with the layer structure of the neural network. In quantum machine learning, the ansatz can be tuned to correspond to the specific symmetry of the problem. We investigate this adaption of the quantum circuit to the problem symmetry on graph classification problems. On random graphs, the quantum machine learning ansatz classifies whether a given random graph is connected, bipartite, contains a Hamiltonian path or cycle, respectively. We find that if the quantum circuit reflects the inherent symmetry of the problem, it vastly outperforms the standard, unsymmetrized ansatzes. Even when the symmetry is only approximative, there is still a significant performance gain over non-symmetrized ansatzes. We show how the symmetry can be included in the quantum circuit in a straightforward constructive method.

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