Proceedings

Optimizing Parameters of Quantum Circuits with Sparsity-Inducing Coordinate Descent

Rudy Raymond, Zichang He

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 6111-6119.

https://doi.org/10.24963/ijcai.2025/680

Keywords

  • Machine Learning: ML: Regression
  • Machine Learning: ML: Learning sparse models
  • Search: S: Combinatorial search and optimisation
  • Machine Learning: ML: Optimization

Abstract

Parameterized Quantum Circuit (PQC) is a family of structured quantum circuits that consists of quantum gates whose parameters are optimized with classical computers. With the quest for a potential speedup, there is a need to run larger quantum circuits, which in turn results in the arduous task of parameter optimization. In this paper, we propose a generic method, called Rotolasso, that utilizes sparsity-inducing coordinate descent (CD) to optimize parameters of a PQC for balancing its accuracy and the number of parameterized gates. The use of CD allows significant reduction in the number of quantum circuit runs, and the sparsity in the model leads to simpler and faster PQCs, both of which are important ingredients to overcome limitations of near-term quantum devices. We provide theoretical analyses and demonstrate experiments showing the effectiveness of Rotolasso to solve instances of combinatorial optimization problems.

Rudy Raymond, and Zichang He. 2025. Optimizing Parameters of Quantum Circuits with Sparsity-Inducing Coordinate Descent. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 6111–6119. https://doi.org/10.24963/ijcai.2025/680
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