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A Full Quantum Eigensolver for Quantum Chemistry Simulations
Author(s) -
Shijie Wei,
Hang Li,
GuiLu Long
Publication year - 2020
Publication title -
research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.8
H-Index - 16
ISSN - 2639-5274
DOI - 10.34133/2020/1486935
Subject(s) - quantum chemistry , quantum computer , quantum , quantum algorithm , quantum simulator , gradient descent , perturbation theory (quantum mechanics) , computer science , electronic structure , algorithm , statistical physics , computational science , chemistry , quantum mechanics , computational chemistry , physics , molecule , supramolecular chemistry , machine learning , artificial neural network
Quantum simulation of quantum chemistry is one of the most compelling applications of quantum computing. It is of particular importance in areas ranging from materials science, biochemistry, and condensed matter physics. Here, we propose a full quantum eigensolver (FQE) algorithm to calculate the molecular ground energies and electronic structures using quantum gradient descent. Compared to existing classical-quantum hybrid methods such as variational quantum eigensolver (VQE), our method removes the classical optimizer and performs all the calculations on a quantum computer with faster convergence. The gradient descent iteration depth has a favorable complexity that is logarithmically dependent on the system size and inverse of the precision. Moreover, the FQE can be further simplified by exploiting a perturbation theory for the calculations of intermediate matrix elements and obtaining results with a precision that satisfies the requirement of chemistry application. The full quantum eigensolver can be implemented on a near-term quantum computer. With the rapid development of quantum computing hardware, the FQE provides an efficient and powerful tool to solve quantum chemistry problems.

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