https://doi.org/10.1140/epjqt/s40507-024-00289-z
Research
KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Search
1
QTF Centre of Excellence, Department of Physics, University of Helsinki, Helsinki, Finland
2
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland
3
Joint Doctoral School, Silesian University of Technology, Gliwice, Poland
4
Quantum Intelligence Alliance, Kolkata, India
5
QuTech, Delft University of Technology, Delft, The Netherlands
6
Raman Research Institute, Bengaluru, India
7
Centre for Quantum Science and Technology (CQST), International Institute of Information Technology, Hyderabad, Telangana, India
Received:
2
August
2024
Accepted:
4
November
2024
Published online:
12
November
2024
Quantum architecture Search (QAS) is a promising direction for optimization and automated design of quantum circuits towards quantum advantage. Recent techniques in QAS emphasize Multi-Layer Perceptron (MLP)-based deep Q-networks. However, their interpretability remains challenging due to the large number of learnable parameters and the complexities involved in selecting appropriate activation functions. In this work, to overcome these challenges, we utilize the Kolmogorov-Arnold Network (KAN) in the QAS algorithm, analyzing their efficiency in the task of quantum state preparation and quantum chemistry. In quantum state preparation, our results show that in a noiseless scenario, the probability of success is 2× to 5× higher than MLPs. In noisy environments, KAN outperforms MLPs in fidelity when approximating these states, showcasing its robustness against noise. In tackling quantum chemistry problems, we enhance the recently proposed QAS algorithm by integrating curriculum reinforcement learning with a KAN structure. This facilitates a more efficient design of parameterized quantum circuits by reducing the number of required 2-qubit gates and circuit depth. Further investigation reveals that KAN requires a significantly smaller number of learnable parameters compared to MLPs; however, the average time of executing each episode for KAN is higher.
Key words: Kolmogorov-Arnold network / Quantum architecture search / Quantum state reconstruction / Quantum chemistry
© The Author(s) 2024
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
