Warm Starting Variational Quantum Algorithms with Near Clifford Circuits

As a mainstream approach in the quantum machine learning field, variational quantum algorithms (VQAs) are frequently mentioned among the most promising applications for quantum computing. However, VQAs suffer from inefficient training methods. Here, we propose a pretraining strategy named near Clifford circuits warm start (NCC-WS) to find the initialization for parameterized quantum circuits (PQCs) in VQAs. We explored the expressibility of NCCs and the correlation between the expressibility and acceleration. The achieved results suggest that NCC-WS can find the correct initialization for the training of VQAs to achieve acceleration.

Automated summary

Variational quantum algorithms (VQAs) are a mainstream approach in the quantum machine learning field and considered one of the most promising applications for quantum computing. However, inefficient training methods hinder the progress of VQAs. This study introduces a pretraining strategy called near Clifford circuits warm start (NCC-WS) to find the initialization for parameterized quantum circuits (PQCs) in VQAs. The results indicate that NCC-WS can achieve acceleration by finding the correct initialization for VQA training.