论文标题
双重分解的有效量子分析核梯度
Efficient Quantum Analytic Nuclear Gradients with Double Factorization
论文作者
论文摘要
哈密顿量的有效表示，例如双重分解大大降低了纠正误差和嘈杂的化学算法中的重复次数和嘈杂的中间尺度量子（NISQ）算法。我们报告了一种基于拉格朗日的方法，用于评估双重分解汉密尔顿人的松弛单粒子和两粒子降低的密度矩阵，从而在计算核梯度和相关衍生性特性方面释放了提高效率。我们证明了我们基于拉格朗日的方法在经典拟合的示例中恢复所有基于二重奏密度矩阵元素的准确性和可行性，其中最多327个量子和18470年的原子在QM/mm模拟中，具有适度的量子量子。我们在案例研究中，例如过渡态优化，从头算分子动力学模拟和大分子系统的能量最小化等案例研究中，我们在差异量子本质量（VQE）的背景下显示了这一点。
Efficient representations of the Hamiltonian such as double factorization drastically reduce circuit depth or number of repetitions in error corrected and noisy intermediate scale quantum (NISQ) algorithms for chemistry. We report a Lagrangian-based approach for evaluating relaxed one- and two-particle reduced density matrices from double factorized Hamiltonians, unlocking efficiency improvements in computing the nuclear gradient and related derivative properties. We demonstrate the accuracy and feasibility of our Lagrangian-based approach to recover all off-diagonal density matrix elements in classically-simulated examples with up to 327 quantum and 18470 total atoms in QM/MM simulations, with modest-sized quantum active spaces. We show this in the context of the variational quantum eigensolver (VQE) in case studies such as transition state optimization, ab initio molecular dynamics simulation and energy minimization of large molecular systems.
