LAUSR

Simulated quantum annealing (SQA) is a probabilistic approximation method to find a solution for a combinatorial optimization problem using digital computers. The processing time of SQA increases exponentially with the number of variables. Therefore, acceleration of SQA is regarded as a very important topic. However, parallel implementation is difficult due to the serial nature of the quantum Monte Carlo algorithm used in SQA. In this paper, we propose a method to implement SQA in parallel on a GPU while preserving the data dependency. According to the experimental results, we have achieved over 97 times speed-up while maintaining the same accuracy-level compared to a single-core CPU implementation.