Adiabatic quantum computing is a universal model for quantum computing whose implementation using a gate-based quantum computer requires depths that are unreachable in the early fault-tolerant era. To mitigate the limitations of near-term devices, a number of hybrid approaches have been pursued in which a parameterized quantum circuit prepares and measures quantum states and a classical optimization algorithm minimizes an objective function that encompasses the solution to the problem of interest. In this work, we propose a different approach starting by analyzing how a small perturbation of a Hamiltonian affects the parameters that minimize the energy within a family of parameterized quantum states. We derive a set of equations that allow us to compute the new minimum by solving a constrained linear system of equations that is obtained from measuring a series of observables on the unperturbed system. We then propose a discrete version of adiabatic quantum computing that can be implemented in a near-term device while at the same time is insensitive to the initialization of the parameters and to other limitations hindered in the optimization part of variational quantum algorithms. We compare our proposed algorithm with the variational quantum eigensolver on two classical optimization problems, namely MaxCut and number partitioning, and on a quantum-spin configuration problem, the transverse-field ising chain model, and confirm that our approach demonstrates superior performance.

中文翻译：

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使用参数化量子电路模拟绝热量子计算


绝热量子计算是一种通用的量子计算模型，使用基于门的量子计算机实现其需要的深度在早期的容错时代是无法达到的。为了减轻近期设备的局限性，已经采用了许多混合方法，其中参数化量子电路准备和测量量子态，而经典优化算法最小化包含感兴趣问题的解决方案的目标函数。在这项工作中，我们提出了一种不同的方法，首先分析哈密顿量的小扰动如何影响最小化参数化量子态族中能量的参数。我们推导出一组方程，这些方程允许我们通过求解一个约束线性方程组来计算新的最小值，该方程组是通过在未受扰动的系统上测量一系列可观察对象而获得的。然后，我们提出了一种离散版本的绝热量子计算，它可以在近期设备中实现，同时对参数的初始化和变分量子算法优化部分阻碍的其他限制不敏感。我们在两个经典优化问题（即 MaxCut 和数分区）以及量子自旋配置问题（横场 ising 链模型）上将我们提出的算法与变分量子特征求解器进行了比较，并确认我们的方法表现出卓越的性能。