Scientists have made a significant advancement with quantum technologies that could transform complex systems modeling with an accurate and effective approach that requires significantly reduced memory.

Complex systems play a vital role in our daily lives, whether that be predicting traffic patterns, weather forecasts, or understanding financial markets. However, accurately predicting these behaviors and making informed decisions relies on storing and tracking vast information from events in the distant past—a process which presents huge challenges.

Current models using artificial intelligence see their memory requirements increase by more than a hundredfold every two years and can often involve optimization over billions—or even trillions—of parameters. Such immense amounts of information lead to a bottleneck where we must trade-off memory cost against predictive accuracy.

A collaborative team of researchers from The University of Manchester, the University of Science and Technology of China (USTC), the Centre for Quantum Technologies (CQT) at the National University of Singapore and Nanyang Technological University (NTU) propose that quantum technologies could provide a way to mitigate this trade-off.

The team have successfully implemented quantum models that can simulate a family of complex processes with only a single qubit of memory—the basic unit of quantum information—offering substantially reduced memory requirements.

To read more, click here.