Recently, a remarkable breakthrough emerged in the realm of quantum machine learning experiments. A team, spearheaded by Professor Peng Xinhua and Associate Researcher Li Zhaokai from the Spin Magnetic Resonance Laboratory at the University of Science and Technology of China (USTC), in collaboration with Professor Li Xiaopeng from Fudan University, made significant strides. In the academic world, the pursuit of innovation is a driving force for progress. This team creatively put forward a brand - new quantum reservoir computing method. This method is rooted in correlated quantum spin systems, which is a highly specialized and cutting - edge area in quantum science. In our daily life, we are constantly dealing with various time - series data, such as stock market fluctuations, weather changes over time, etc. Predicting these real - world time - series tasks accurately is of great importance. This team, through their experiments, verified for the very first time that when it comes to handling such real - world time - series prediction tasks, quantum machine learning can outperform classical neural network models. Classical neural network models have long been a mainstream approach in data prediction, but quantum machine learning, with its unique quantum properties, has shown its potential to revolutionize this field. The related research findings were published on March 25, 2026, marking a significant milestone in the development of quantum machine learning.