QAS-BO: Quantum Architecture Search Based on Bayesian Optimization Applied to Variational Quantum Algorithms

In the era of Noisy Intermediate-Scale Quantum (NISQ) computing, traditional quantum algorithms face the challenges of limited number of qubits, noise and decoherence. In order to address these issues, we propose a Quantum Architecture Search (QAS) method driven by Bayesian Optimization (BO), which is applied to variational quantum algorithms. In this work, QAS is regarded as a fixed-scale sampling problem. We innovatively propose a quantum gate pool and use a parameterized probabilistic model to dynamically determine the optimal quantum gate for each position in the quantum circuit, thus optimizing the circuit structure. Through using a gradient-free BO method based on radial basis function, we adaptively design end-to-end quantum circuits, significantly reducing circuit depths and improving computational accuracy. We conducted experiments on ground state energy estimation in quantum chemistry and combinatorial optimization problem. The experimental results show that our method is significantly superior to traditional methods and other meta-heuristic search methods in accuracy and efficiency. Our method not only reduces the depth of quantum circuits by up to 85% under a certain accuracy, but also improves the accuracy rate to nearly 100% in combinatorial optimization problem. This provides a powerful and efficient tool for designing optimal quantum circuits and promotes the practical application of quantum algorithms in the NISQ era.