Quantum architecture search~(QAS) is a promising direction for optimization
and automated design of quantum circuits towards quantum advantage. Recent
techniques in QAS focus on machine learning-based approaches from reinforcement
learning, like deep Q-network. While multi-layer perceptron-based deep
Q-networks have been applied for QAS, their interpretability remains
challenging due to the high number of parameters. In this work, we evaluate the
practicality of KANs in quantum architecture search problems, analyzing their
efficiency in terms of the probability of success, frequency of optimal
solutions and their dependencies on various degrees of freedom of the network.
In a noiseless scenario, the probability of success and the number of optimal
quantum circuit configurations to generate the multi-qubit maximally entangled
states are significantly higher than MLPs. Moreover in noisy scenarios, KAN can
achieve a better fidelity in approximating maximally entangled state than MLPs,
where the performance of the MLP significantly depends on the choice of
activation function. Further investigation reveals that KAN requires a very
small number of learnable parameters compared to MLPs, however, the average
time of executing each episode for KAN is much higher.

评估 KAN 在量子架构搜索问题中的实用性和效率，发现其在干扰场景中比多层感知器（MLPs）更有效地生成多量子位最大纠缠态并较好地逼近最大纠缠态，但需要更多的执行时间。