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arXiv 2301.01778quant-phcs.DSmath.OC
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利用费米子嵌入扩展量子优化的适用范围

Expanding the reach of quantum optimization with fermionic embeddings

Andrew Zhao, Nicholas C. Rubin

AI总结:

针对正交矩阵二次规划缺乏有效量子表示的问题,提出将其嵌入费米子哈密顿量以实现量子松弛,仅需线性数量量子比特,并设计了舍入程序,数值实验证明该方法能产生高质量近似解。

摘要:

正交矩阵上的二次规划包含一大类缺乏有效量子表示的困难优化问题。此类问题是小非交换 Grothendieck 问题(LNCG)的实例,即二元二次规划向连续、非交换变量的推广。在本研究中,我们为这类 LNCG 问题建立了一种到费米子哈密顿量的自然嵌入,从而能够使用量子信息工具来研究这一经典问题。该嵌入是通过将正交矩阵表示为费米子量子态的新方法实现的,我们通过正交群著名的双重覆盖完成了这一点。相应地,嵌入后的 LNCG 哈密顿量是一个两体费米子模型。确定该哈密顿量的极值态为原问题提供了一个外部近似,这是经典半正定松弛的量子类比。特别是,当在特殊正交群上进行优化时,我们的量子松弛遵循基于旋转矩阵凸包的额外强大约束。这种凸包表示的经典规模随矩阵维度呈指数增长,而我们的量子表示仅需线性数量的量子比特。最后,为了将松弛后的解投影回可行空间,我们提出了舍入程序,通过对量子态的适当测量返回正交矩阵。通过数值实验,我们提供了证据表明这种舍入后的量子松弛能够产生高质量的近似解。

英文摘要:

Quadratic programming over orthogonal matrices encompasses a broad class of hard optimization problems that do not have an efficient quantum representation. Such problems are instances of the little noncommutative Grothendieck problem (LNCG), a generalization of binary quadratic programs to continuous, noncommutative variables. In this work, we establish a natural embedding for this class of LNCG problems onto a fermionic Hamiltonian, thereby enabling the study of this classical problem with the tools of quantum information. This embedding is accomplished by a new representation of orthogonal matrices as fermionic quantum states, which we achieve through the well-known double covering of the orthogonal group. Correspondingly, the embedded LNCG Hamiltonian is a two-body fermion model. Determining extremal states of this Hamiltonian provides an outer approximation to the original problem, a quantum analogue to classical semidefinite relaxations. In particular, when optimizing over the \emph{special} orthogonal group our quantum relaxation obeys additional, powerful constraints based on the convex hull of rotation matrices. The classical size of this convex-hull representation is exponential in matrix dimension, whereas our quantum representation requires only a linear number of qubits. Finally, to project the relaxed solution back into the feasible space, we propose rounding procedures which return orthogonal matrices from appropriate measurements of the quantum state. Through numerical experiments we provide evidence that this rounded quantum relaxation can produce high-quality approximations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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