Wedoany.com Report on 9th, Researchers from the American quantum computing company IonQ and Microsoft recently co-authored an article in the authoritative journal "IEEE Spectrum," proposing a novel research method that integrates quantum computing and artificial intelligence. This method involves using quantum computers to generate precise data for training AI systems, thereby accelerating chemical simulations and the discovery of new materials. This hybrid computing approach aims to break through the "exponential wall" bottleneck encountered by traditional supercomputers when simulating molecular electronic behavior, opening up new possibilities for materials science and drug development.
The article points out that the core advantage of quantum computers lies in their ability to directly simulate the quantum behavior of electrons, generating high-precision training data that is currently difficult for classical computers to achieve. This data can then be used to train machine learning models. Once trained, the AI models can quickly predict chemical properties on ordinary classical computers, significantly improving simulation efficiency. Using the famous "Jacob's ladder" in quantum chemistry as a metaphor, the researchers vividly explained how this method bypasses the extremely time-consuming density functional or wave function calculation steps in traditional computing, directly achieving high accuracy.
The article cites a previous classic case by Microsoft and the Pacific Northwest National Laboratory as evidence: within one week, an AI model screened approximately 800 promising battery electrolytes from 32 million candidate materials and successfully synthesized one sodium-based solid electrolyte, significantly reducing reliance on lithium resources. The researchers believe that training AI models with data generated by quantum computing could further enhance model accuracy, enabling them to capture more complex electronic interactions and thereby screen for new materials with superior performance.
Although current quantum computers still face challenges in scaling up and improving fault tolerance, the article emphasizes that this hybrid method allows research institutions to gradually integrate applications based on the development stage of quantum hardware. As the number of qubits increases and fidelity improves, the quality of training data available to AI models will also improve accordingly.
At the end of the article, the researchers stated that the combination of quantum computing and artificial intelligence has the potential to fundamentally transform the way chemical modeling is performed. In the future, high-precision chemical simulations may be run on ordinary computers, which will greatly advance industries such as pharmaceuticals, energy, and materials that rely on molecular design.
