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This article centers on using artificial intelligence to automate classical optimization routines within variational quantum algorithms, directly addressing a core computational bottleneck in near-term quantum hardware. By deploying reinforcement learning agents and neural networks to replace manual tuning, the research aims to significantly improve the scalability of quantum simulations for chemistry and materials science. This represents a critical convergence where AI serves as an essential accelerator for advancing practical quantum computing capabilities.
Rice Builds AI to Tackle Quantum Optimization Bottlenecks
Rice University received 2 DOE Genesis Mission awards to apply artificial intelligence to challenges in quantum computing, specifically variational quantum algorithms.
Rice University has secured two Phase I awards from the U.S. Department of Energy’s Genesis Mission, focusing investment on artificial intelligence and scientific discovery. The projects, led by Anastasios Kyrillidis and Caroline Ajo-Franklin, will apply AI to address critical bottlenecks in both quantum computing and sustainable bioproduction. Kyrillidis’ team aims to improve the performance of variational quantum algorithms, currently hampered by classical optimization methods that require significant computational resources. Amy Dittmar, the Howard R. Hughes Provost and executive vice president for academic affairs at Rice, stated that these awards reflect Rice’s ability to bring…
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