https://doi.org/10.1140/epjqt/s40507-024-00265-7
Research
Continuous evolution for efficient quantum architecture search
1
School of Information Systems Engineering, Information Engineering University, 62 Science Avenue, 450001, Zhengzhou, China
2
Laboratory for Advanced Computing and Intelligence Engineering, 450001, Zhengzhou, China
3
School of Electrical and Information Engineering, Zhengzhou University, 100 Science Avenue, 450001, Zhengzhou, China
b
hcl_xdspeechlab@aliyun.com
h
qudan_xd@163.com
Received:
20
November
2023
Accepted:
26
August
2024
Published online:
6
September
2024
Variational quantum algorithms (VQAs) have been successfully applied to quantum approximate optimization algorithms, variational quantum compiling, and quantum machine learning models. The performance of VQAs is significantly influenced by the architecture of parameterized quantum circuits (PQCs). Quantum architecture search aims to automatically discover high-performance quantum circuits for specific VQA tasks. Quantum architecture search algorithms that utilize both SuperCircuit training and a parameter-sharing approach can save computational resources. If we directly follow the parameter-sharing approach, the SuperCircuit has to be trained to compensate for the worse search space. To address the challenges posed by the worse search space, we introduce an optimization strategy known as the efficient continuous evolutionary approach using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Then, we leverage prior information (symmetric property) designing Structure Symmetric Pruning for removing redundant gates of the searched ansatz. Experiments show that the efficient continuous evolutionary approach can search for more quantum architectures with better performance; the number of high-performance ansatzes obtained by our method is 10% higher than that in the literature (Du et al. in npj Quantum Inf. 8:62, 2022). The application of Structure Symmetric Pruning effectively reduces the number of parameters in quantum circuits without compromising their performance significantly. In binary classification tasks, the pruned quantum circuits exhibit an average accuracy reduction of 0.044 compared to their unpruned counterparts.
Key words: Quantum architecture search / NSGA-II / Symmetric pruning
© The Author(s) 2024
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