论文标题

嘈杂的中间量子计算机上的平行量子化学

Parallel Quantum Chemistry on Noisy Intermediate-Scale Quantum Computers

论文作者

Schade, Robert, Bauer, Carsten, Tamoev, Konstantin, Mazur, Lukas, Plessl, Christian, Kühne, Thomas D.

论文摘要

提出了一种新型的平行杂种量子量子算法,用于在基于栅极的量子计算机上量子化地基态问题解决方案。该方法基于电子结构问题的降低密度 - 静脉功能理论(RDMFT)的表述。为此,使用自适应群集近似RDMFT的近似值,将完整系统的密度 - 矩阵功能分解为其所有子系统的密度 - 矩阵功能的间接耦合总和。分解和自适应群集近似本身所涉及的近似值可以系统地收敛到确切的结果。有效子系统的密度 - 静音功能的溶液涉及许多粒子状态的约束最小化,这些粒子状态与量子计算机上的参数化试验状态近似于量子量量子量相似。有效子系统的密度 - 矩阵功能的独立性引入了新的并行化水平,并允许使用给定的Qubit计数在量子计算机上对大量分子进行计算处理。此外,提出了提出的算法技术,以减少量子计数,量子程序的数量及其深度。对于基于超导式transmon Qubt的IBM量子计算机上的类似Hubbard的系统,已证明了新方法。

A novel parallel hybrid quantum-classical algorithm for the solution of the quantum-chemical ground-state energy problem on gate-based quantum computers is presented. This approach is based on the reduced density-matrix functional theory (RDMFT) formulation of the electronic structure problem. For that purpose, the density-matrix functional of the full system is decomposed into an indirectly coupled sum of density-matrix functionals for all its subsystems using the adaptive cluster approximation to RDMFT. The approximations involved in the decomposition and the adaptive cluster approximation itself can be systematically converged to the exact result. The solutions for the density-matrix functionals of the effective subsystems involves a constrained minimization over many-particle states that are approximated by parametrized trial states on the quantum computer similarly to the variational quantum eigensolver. The independence of the density-matrix functionals of the effective subsystems introduces a new level of parallelization and allows for the computational treatment of much larger molecules on a quantum computer with a given qubit count. In addition, for the proposed algorithm techniques are presented to reduce the qubit count, the number of quantum programs, as well as its depth. The new approach is demonstrated for Hubbard-like systems on IBM quantum computers based on superconducting transmon qubits.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源