GoKawiilA new study led by researchers at UCL (University College London) shows that combining quantum computing with artificial intelligence can significantly improve predictions of complex physical systems over long periods. The hybrid approach outperforms leading models that rely only on conventional computers.
The results, published in Science Advances, could enhance simulations of how liquids and gases behave, known as fluid dynamics. These types of models are essential in fields such as climate science, transportation, medicine, and energy production.
Why Quantum Computing Makes a Difference
The improved accuracy appears to come from how quantum computers process information. Unlike traditional computers that use bits set to either 1 or 0, quantum computers use qubits, which can exist as 1, 0, or anything in between. In addition, each qubit can influence others, allowing a relatively small number of qubits to represent an enormous number of possible states.
Professor Peter Coveney, senior author from UCL Chemistry and the Advanced Research Computing Centre, explained the challenge: "To make predictions about complex systems, we can either run a full simulation, which might take weeks -- often too long to be useful -- or we can use an AI model which is quicker but more unreliable over longer time scales.
"Our quantum-informed AI model means we could provide more accurate predictions quickly. Making predictions about fluid flow and turbulence is a fundamental science challenge but it also has many applications. Our method can be used in climate forecasting, in modeling blood flow and the interaction of molecules, or to better design wind farms so they generate more energy."
How the Hybrid Quantum-AI Method Works
Although quantum computers are widely expected to surpass classical machines in power, their real-world use has so far been limited. This new approach integrates quantum computing into a specific stage of the AI training process.
Typically, AI models learn from large datasets generated by simulations or observations. In this case, the data is first processed by a quantum computer, which identifies key statistical patterns that remain stable over time. These patterns, known as invariant statistical properties, are then used to guide the training of an AI model running on a conventional supercomputer.
Higher Accuracy With Less Memory
... continue reading