A team led by Academician GUO Guangcan from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences (CAS), with collaborative efforts from the University of Manchester, and Nanyang Technological University, has achieved new progress in applying quantum technologies in complex systems modeling. The results were published in Nature Communications on May 6.
Stochastic modeling can help us to predict the future behavior of complex processes, which are non-Markovian. In order to simulate a non-Markovian process, a memory is of necessity to store a large amount of observed information about the past of the system. However, it remains elusive to reduce the memory while ensuring the accuracy of predictions.
Researchers demonstrated that their quantum model based on photonic systems can simulate any process in non-Markovian stochastic processes using only a single quantum bit, even in the presence of experimental noise. The quantum model was capable of making more accurate predictions of future behaviors than the optimal classical model with the same memory dimension.
This study successfully demonstrates the storage benefits of quantum technology for modeling non-Markovian processes in complex systems. This is also a key step toward demonstrating the scalability and robustness of quantum memory advantage.
Implementing quantum dimensionality reduction for non-Markovian stochastic simulation
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