Department of Physics, Co-Design Center for Quantum Advantage, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
J Chem Theory Comput. 2023 Apr 25;19(8):2230-2247. doi: 10.1021/acs.jctc.3c00012. Epub 2023 Mar 31.
We extend molecular bootstrap embedding to make it appropriate for implementation on a quantum computer. This enables solution of the electronic structure problem of a large molecule as an optimization problem for a composite Lagrangian governing fragments of the total system, in such a way that fragment solutions can harness the capabilities of quantum computers. By employing state-of-art quantum subroutines including the quantum SWAP test and quantum amplitude amplification, we show how a quadratic speedup can be obtained over the classical algorithm, in principle. Utilization of quantum computation also allows the algorithm to match─at little additional computational cost─full density matrices at fragment boundaries, instead of being limited to 1-RDMs. Current quantum computers are small, but quantum bootstrap embedding provides a potentially generalizable strategy for harnessing such small machines through quantum fragment matching.
我们将分子引导嵌入方法进行扩展，使其适用于量子计算机的实现。这使得可以将大分子的电子结构问题作为一个复合拉格朗日的优化问题来解决，该拉格朗日控制着整个系统的片段，从而使得片段解决方案可以利用量子计算机的能力。通过采用最先进的量子子程序，包括量子 SWAP 测试和量子振幅放大，我们展示了如何在原则上从经典算法中获得二次加速。量子计算的利用还允许该算法在附加计算成本很小的情况下匹配片段边界处的完整密度矩阵，而不是限于 1-RDM。当前的量子计算机很小，但是分子引导嵌入方法提供了一种通过量子片段匹配利用这种小型机器的潜在可扩展策略。
