KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Search

Abstract

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 times 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.

Code available at: https://github.com/Aqasch/KANQAS_code

References in corpus (7)

• Ground state energy functional with Hartree-Fock efficiency and chemical accuracy
• Reinforcement Learning Assisted Recursive QAOA
• Distributed quantum architecture search
• DeepOKAN: Deep Operator Network Based on Kolmogorov Arnold Networks for Mechanics Problems
• Enhancing variational quantum state diagonalization using reinforcement learning techniques
• Quantum State Reconstruction in a Noisy Environment via Deep Learning
• A quantum information theoretic analysis of reinforcement learning-assisted quantum architecture search

Cited by in corpus (5)

• Adaptive Training of Grid-Dependent Physics-Informed Kolmogorov-Arnold Networks
• A Practitioner's Guide to Kolmogorov-Arnold Networks
• ChemKANs for Combustion Chemistry Modeling and Acceleration
• Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
• QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation