KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Search
Akash Kundu, Aritra Sarkar, Abhishek Sadhu

TL;DR
This paper introduces KANQAS, a quantum architecture search method using Kolmogorov-Arnold Networks, which improves quantum state preparation success rates and circuit efficiency over traditional MLP-based approaches, especially in noisy environments.
Contribution
The paper proposes integrating Kolmogorov-Arnold Networks into quantum architecture search, enhancing interpretability, robustness, and efficiency in quantum circuit design compared to existing MLP-based methods.
Findings
KAN outperforms MLPs in noiseless quantum state preparation
KAN achieves higher fidelity in noisy environments
KAN reduces the number of 2-qubit gates and circuit depth
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…
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Taxonomy
TopicsTime Series Analysis and Forecasting
MethodsFocus
