Dynamical Self-energy Mapping (DSEM) for Creation of Sparse Hamiltonians Suitable for Quantum Computing
Dynamical Self-energy Mapping (DSEM) for Creation of Sparse Hamiltonians Suitable for Quantum Computing
复制标题
用于创建适合量子计算的稀疏哈密顿量的动态自能映射 (DSEM)
DOI：
10.1021/acs.jctc.1c00931
复制
发表时间：
2021
影响因子：
5.5
通讯作者：
Zgid, Dominika
中科院分区：
文献类型：
--
作者：
Dhawan, Diksha;Metcalf, Mekena;Zgid, Dominika
我们提出了一个两步的程序称为动态自能映射（DSEM），使我们能够找到一个稀疏的分子问题的哈密顿表示。在这个过程的第一部分中，分子系统的近似自能使用低水平的方法进行评估，随后发现稀疏哈密顿量最好地恢复这种低水平的动态自能。在第二步中，这样的稀疏哈密尔顿算子被一个高级方法使用，该方法提供了在以后的计算中采用的自能的高度精确的动力学部分。小分子问题的测试表明，稀疏哈密顿参数化导致非常好的总能量。DSEM有可能被用作量子计算的经典-量子混合算法，其中在高斯轨道基础上仅包含O（n2）项的稀疏哈密顿量，其中是系统中轨道的数量，与涉及完整哈密顿量的模拟相比，可以将量子电路的深度减少至少一个数量级。
We present a two-step procedure called the dynamical self-energy mapping (DSEM) that allows us to find a sparse Hamiltonian representation for molecular problems. In the first part of this procedure, the approximate self-energy of a molecular system is evaluated using a low-level method and subsequently a sparse Hamiltonian is found that best recovers this low-level dynamic self-energy. In the second step, such a sparse Hamiltonian is used by a high-level method that delivers a highly accurate dynamical part of the self-energy that is employed in later calculations. The tests conducted on small molecular problems show that the sparse Hamiltonian parameterizations lead to very good total energies. DSEM has the potential to be used as a classical–quantum hybrid algorithm for quantum computing where the sparse Hamiltonian containing onlyO(n2) terms on a Gaussian orbital basis, wherenis the number of orbitals in the system, could reduce the depth of the quantum circuit by at least an order of magnitude when compared with simulations involving a full Hamiltonian.