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Minor-embedding in adiabatic quantum computation: I. The parameter setting problem

机译:绝热量子计算中的小嵌入:I.参数设置问题

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We show that the NP-hard quadratic unconstrained binary optimization (QUBO) problem on a graph G can be solved using an adiabatic quantum computer that implements an Ising spin-1/2 Hamiltonian, by reduction through minor-embedding of G in the quantum hardware graph U. There are two components to this reduction: embedding and parameter setting. The embedding problem is to find a minor-embedding G emb of a graph G in U, which is a subgraph of U such that G can be obtained from G emb by contracting edges. The parameter setting problem is to determine the corresponding parameters, qubit biases and coupler strengths, of the embedded Ising Hamiltonian. In this paper, we focus on the parameter setting problem. As an example, we demonstrate the embedded Ising Hamiltonian for solving the maximum independent set (MIS) problem via adiabatic quantum computation (AQC) using an Ising spin-1/2 system. We close by discussing several related algorithmic problems that need to be investigated in order to facilitate the design of adiabatic algorithms and AQC architectures.
机译:我们展示了可以通过使用实现Ising自旋1/2哈密顿量的绝热量子计算机来解决图G上的NP硬二次无约束二进制优化(QUBO)问题,方法是通过对量子硬件中的G进行小嵌入来进行还原图U。这种减少有两个组成部分:嵌入和参数设置。嵌入问题是在U中找到图G的次嵌入G emb ,它是U的子图,因此可以通过收缩边从G emb 获得G。参数设置问题是确定嵌入的伊辛哈密顿量的相应参数,量子位偏置和耦合器强度。在本文中,我们集中于参数设置问题。例如,我们演示了嵌入式Ising哈密顿量,该方法用于使用Ising spin-1 / 2系统通过绝热量子计算(AQC)解决最大独立集(MIS)问题。最后,我们讨论了一些相关的算法问题,这些问题需要研究才能促进绝热算法和AQC体系结构的设计。

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