arXiv 2009.025592020-09-05quant-ph

动力学量子模拟的变分哈密顿对角化

Variational Hamiltonian Diagonalization for Dynamical Quantum Simulation

Benjamin Commeau, M. Cerezo, Zoë Holmes, Lukasz Cincio, Patrick J. Coles, Andrew Sornborger

备注：: 7+5 pages, 4+1 figures

摘要：

动态量子模拟可能是最早看到量子优势的应用之一。然而，标准特洛特化方法的电路深度可以迅速超过有噪声的量子计算机的相干时间。这导致了最近的建议变分方法的动态模拟。在这项工作中，我们的目标是使变分动力学模拟更加实用和短期。我们提出了一个新的算法称为变分哈密顿对角化（VHD），近似地将一个给定的哈密顿转换成一个对角线的形式，可以很容易地求幂。VHD允许快速转发，即，在具有固定深度的量子线路的量子计算机的相干时间之外的模拟。它还消除了Trotterization误差，并允许模拟整个希尔伯特空间。我们证明了VHD成本函数的平均仿真保真度的操作意义。此外，我们证明了VHD成本函数不表现出浅深度的贫瘠高原，即，它的梯度不是指数地消失。我们的证明依赖于哈密尔顿算子的局部性，因此我们将局部性与可训练性联系起来。我们的数值模拟验证了VHD可以用于快进动力学。

英文摘要：

Dynamical quantum simulation may be one of the first applications to see quantum advantage. However, the circuit depth of standard Trotterization methods can rapidly exceed the coherence time of noisy quantum computers. This has led to recent proposals for variational approaches to dynamical simulation. In this work, we aim to make variational dynamical simulation even more practical and near-term. We propose a new algorithm called Variational Hamiltonian Diagonalization (VHD), which approximately transforms a given Hamiltonian into a diagonal form that can be easily exponentiated. VHD allows for fast forwarding, i.e., simulation beyond the coherence time of the quantum computer with a fixed-depth quantum circuit. It also removes Trotterization error and allows simulation of the entire Hilbert space. We prove an operational meaning for the VHD cost function in terms of the average simulation fidelity. Moreover, we prove that the VHD cost function does not exhibit a shallow-depth barren plateau, i.e., its gradient does not vanish exponentially. Our proof relies on locality of the Hamiltonian, and hence we connect locality to trainability. Our numerical simulations verify that VHD can be used for fast-forwarding dynamics.

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