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arXiv 2008.05592quant-phcond-mat.other
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量子计算机上的密度泛函和Kohn-Sham势

Density functionals and Kohn-Sham potentials with minimal wavefunction preparations on a quantum computer

Thomas E. Baker, David Poulin

摘要:

量子计算机的潜在应用之一是解决量子化学系统。众所周知,经典地获得稍微精确的解的最快方法之一是使用密度泛函理论的近似。我们展示了一种从足够强大的量子计算机中获得精确泛函作为机器学习模型的一般方法。仅使用量子计算机上解决方案的当前可行性的现有假设。包括量子相位估计、量子幅度估计和量子梯度方法的几种已知算法用于训练机器学习模型。这种算法组合的一个优点是量子波函数不需要在每一步都完全重新准备,从而降低了相当大的前置因子。利用量子计算机上的基态算法的解决方案的假设,我们证明,找到科恩-沙姆势不一定比基态密度更困难。一旦构造,经典用户可以使用所得到的机器学习泛函自洽地求解系统的基态,只要机器学习近似对于输入系统足够准确。它还演示了如何经典的用户可以访问常用的时间和温度依赖的近似从基态模型。对算法的微小修改可以学习其他类型的函数理论,包括精确的时间和温度依赖性。其他几种算法-包括量子机器学习-在这个问题的一般情况下被证明是不切实际的。

英文摘要:

One of the potential applications of a quantum computer is solving quantum chemical systems. It is known that one of the fastest ways to obtain somewhat accurate solutions classically is to use approximations of density functional theory. We demonstrate a general method for obtaining the exact functional as a machine learned model from a sufficiently powerful quantum computer. Only existing assumptions for the current feasibility of solutions on the quantum computer are used. Several known algorithms including quantum phase estimation, quantum amplitude estimation, and quantum gradient methods are used to train a machine learned model. One advantage of this combination of algorithms is that the quantum wavefunction does not need to be completely re-prepared at each step, lowering a sizable pre-factor. Using the assumptions for solutions of the ground-state algorithms on a quantum computer, we demonstrate that finding the Kohn-Sham potential is not necessarily more difficult than the ground state density. Once constructed, a classical user can use the resulting machine learned functional to solve for the ground state of a system self-consistently, provided the machine learned approximation is accurate enough for the input system. It is also demonstrated how the classical user can access commonly used time- and temperature-dependent approximations from the ground state model. Minor modifications to the algorithm can learn other types of functional theories including exact time- and temperature-dependence. Several other algorithms--including quantum machine learning--are demonstrated to be impractical in the general case for this problem.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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