Kanqas: Kolmogorov-arnold network for quantum architecture search

Akash Kundu, Aritra Sarkar, Abhishek Sadhu

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× 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.