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arXiv 2103.08602quant-phcond-mat.mtrl-scimath-phmath.MPphysics.comp-ph
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低深度Trotter-Suzuki分解的图优化视角

Graph Optimization Perspective for Low-Depth Trotter-Suzuki Decomposition

Albert T. Schmitz, Nicolas P. D. Sawaya, Sonika Johri, A. Y. Matsuura

AI总结:

本文提出将Trotter-Suzuki分解映射到Pauli框架图上的受限路径,利用贪心搜索在Clifford+RZ门集下优化分解顺序,显著降低电路深度和双量子比特门数,最多减少一个数量级,并具有良好的并行化可扩展性。

摘要:

哈密顿量模拟在量子机器学习、量子线性代数方法以及物理、材料科学和化学建模等一大类量子算法与模拟中是一个重要模块。实现时间演化酉算子的最突出方法之一是通过Trotter-Suzuki分解。然而,对于无穷小时间演化算子,由于哈密顿量各项的实施顺序是任意的,因此存在一大类可能的分解。我们引入一种新视角,在标准Clifford+RZ门集假设下,通过借鉴量子纠错的思想来生成低深度Trotter-Suzuki分解。我们将给定的Trotter-Suzuki分解映射到一个图上的受限路径,该图我们称之为Pauli框架图(PFG)。PFG的每个节点表示当前可应用的哈密顿量项集合,Clifford操作表示从一个节点到另一个节点的移动,因此图距离表示实现该分解的门开销。寻找最优分解的问题等价于求解一个类似于旅行商的问题。尽管这是一个NP难问题,我们展示了最简单的启发式方法——贪心搜索,并在一大类科学相关的哈密顿量上,将所得的双量子比特门数和电路深度与更标准的方法进行比较,这些哈密顿量包括费米子和玻色子体系,出现在化学、振动和凝聚态问题中,并自然具有可扩展性。我们发现在几乎所有研究的情形中,所得深度和双量子比特门数都少于标准方法提供的结果,最多可减少一个数量级。我们还发现该方法高效且易于并行化,使其对于真正感兴趣的问题具有可扩展性。

英文摘要:

Hamiltonian simulation represents an important module in a large class of quantum algorithms and simulations such as quantum machine learning, quantum linear algebra methods, and modeling for physics, material science and chemistry. One of the most prominent methods for realizing the time-evolution unitary is via the Trotter-Suzuki decomposition. However, there is a large class of possible decompositions for the infinitesimal time-evolution operator as the order in which the Hamiltonian terms are implemented is arbitrary. We introduce a novel perspective for generating a low-depth Trotter-Suzuki decomposition assuming the standard Clifford+RZ gate set by adapting ideas from quantum error correction. We map a given Trotter-Suzuki decomposition to a constrained path on a graph which we deem the Pauli Frame Graph (PFG). Each node of the PFG represents the set of possible Hamiltonian terms currently available to be applied, Clifford operations represent a move from one node to another, and so the graph distance represents the gate cost of implementing the decomposition. The problem of finding the optimal decomposition is then equivalent to solving a problem similar to the traveling salesman. Though this is an NP-hard problem, we demonstrate the simplest heuristic, greedy search, and compare the resulting two-qubit gate count and circuit depth to more standard methods for a large class of scientifically relevant Hamiltonians, both fermionic and bosonic, found in chemical, vibrational and condensed matter problems which naturally scale. We find in nearly every case we study, the resulting depth and two-qubit gate counts are less than those provided by standard methods, by as much as an order of magnitude. We also find the method is efficient and amenable to parallelization, making the method scalable for problems of real interest.

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