论文标题
量子辅助图聚类和二次无约束的D- ARY优化
Quantum-Assisted Graph Clustering and Quadratic Unconstrained D-ary Optimisation
论文作者
论文摘要
最近，我们目睹了量子信息处理中的壮观发展，并提供了不同体系结构的嘈杂中间尺度量子设备和各种软件开发套件，可用于量子算法。不同的问题很难通过经典计算来解决，但也可以加速（在某些情况下很大）也被人口组成。在利用这些方面，本文研究了通过量子算法或更精确的量子辅助算法研究无监督的图群集。通过仔细检查量子iSing模型框架内的两个群集最大切割问题，通过鉴定适当的哈密顿量，已经为最大3切割制定了扩展。在进行了广泛的数值评估后，已经提供了代表性的结果，其中包括建议使用QUTRIT设备实施可能的未来派实施的建议。此外，还通过一些初步观察，可以通过Qudits（可以通过Qudits来处理）的3个类别的外推到3种以上的类别。在这种情况下，量子辅助解决二次无约束的D- ARY优化的解决。作为额外的新颖性，系统地构建了一个QUDIT电路通过量子近似优化算法切割最大值。
Of late, we are witnessing spectacular developments in Quantum Information Processing with the availability of Noisy Intermediate-Scale Quantum devices of different architectures and various software development kits to work on quantum algorithms. Different problems, which are hard to solve by classical computation, but can be sped up (significantly in some cases) are also being populated. Leveraging these aspects, this paper examines unsupervised graph clustering by quantum algorithms or, more precisely, quantum-assisted algorithms. By carefully examining the two cluster Max-Cut problem within the framework of quantum Ising model, an extension has been worked out for max 3-cut with the identification of an appropriate Hamiltonian. Representative results, after carrying out extensive numerical evaluations, have been provided including a suggestion for possible futuristic implementation with qutrit devices. Further, extrapolation to more than 3 classes, which can be handled by qudits, of both annealer and gate-circuit varieties, has also been touched upon with some preliminary observations; quantum-assisted solving of Quadratic Unconstrained D-ary Optimisation is arrived at within this context. As an additional novelty, a qudit circuit to solve max-d cut through Quantum Approximate Optimization algorithm is systematically constructed.
