EAGER: QAC-QSA: Resource Reduction in Quantum Computational Chemistry Mapping by Optimizing Orbital Basis Sets

点击翻译按钮获取中文摘要

Jianfeng Lu of Duke University is supported by the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to conduct a project on resource reduction in quantum computational mapping by optimizing orbital basis sets. The proposal was submitted in response to the Quantum Algorithm Challenge Dear Colleague Letter, NSF 20-056. Jianfeng Lu and his research group are pursuing novel algorithms to reduce the resource requirements for carrying out quantum chemistry computations on a quantum computer. The research aims to provide state-of-the-art computational methods that push the current boundary of quantum computational chemistry. The project also includes new curriculum development that creates an ideal training platform for a new generation of computational scientists who will be able to understand and contribute to quantum computing and related fields across science and engineering. Quantum chemistry is a natural promising area of applications of quantum computing and has seen many exciting developments in recent years together with experimental demonstration of small-scale problems on actual quantum devices. The current bottleneck for capability of quantum computational chemistry lies in the limited resources provided by the current and near-future NISQ hardware. This bottleneck provides exciting and unprecedented opportunities for novel algorithmic advance and breakthroughs. The proposed research aims at resource reduction by optimizing basis sets for mapping quantum chemistry problem to quantum computer, using a systematic and efficient variational approach, which extends the current variational quantum eigensolver framework. The optimal basis set will be pursued using novel optimization techniques for ground and excited states. Efficient classical-quantum hybrid algorithms will be developed to obtain accurate quantum chemistry calculations for large molecules with large basis sets under NISQ budget constraints. Such innovations, combined with other advances in quantum computational chemistry, would help scale up the ability of quantum computer for quantum chemistry problems beyond the reach of classical algorithms.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.