论文标题
通过高效且稳健的量子可能梯度，最大样式的哈密顿学习学习
Maximum-Likelihood-Estimate Hamiltonian learning via efficient and robust quantum likelihood gradient
论文作者
论文摘要
鉴于量子技术的最新发展，对目标量子多体系统的物理哈密顿量进行建模正在成为越来越实用且重要的研究方向。在这里，我们提出了一种有效的策略，结合了最大似然估计，梯度下降和量子多体算法。鉴于测量结果，我们通过沿量子可能性梯度的一系列下降来优化目标模型哈密顿量和密度算子，我们证明相对于负LOG类函数，我们证明这是负半定准则。因此，除了这种优化效率外，我们的最大可能性静态的哈密顿学习还尊重给定量子系统的局部性，它很容易扩展到具有可用量子多体算法的较大系统。与以前的方法相比，它还表现出更高的准确性和对噪音，波动和温度范围的整体稳定性，我们以各种示例证明了这一点。
Given the recent developments in quantum techniques, modeling the physical Hamiltonian of a target quantum many-body system is becoming an increasingly practical and vital research direction. Here, we propose an efficient strategy combining maximum likelihood estimation, gradient descent, and quantum many-body algorithms. Given the measurement outcomes, we optimize the target model Hamiltonian and density operator via a series of descents along the quantum likelihood gradient, which we prove is negative semi-definite with respect to the negative-log-likelihood function. In addition to such optimization efficiency, our maximum-likelihood-estimate Hamiltonian learning respects the locality of a given quantum system, therefore, extends readily to larger systems with available quantum many-body algorithms. Compared with previous approaches, it also exhibits better accuracy and overall stability toward noises, fluctuations, and temperature ranges, which we demonstrate with various examples.
