Networks of non-equilibrium condensates for global optimization
Networks of non-equilibrium condensates for global optimization
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DOI：
10.1088/1367-2630/aae8ae
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发表时间：
2018-11-14
影响因子：
3.3
通讯作者：
Berloff, Natalia G.
中科院分区：
文献类型：
--
作者：
Kalinin, Kirill P.;Berloff, Natalia G.
最近几个增益耗散平台的基础上的网络的光学参量振荡器，激光器和各种非平衡玻色-爱因斯坦凝聚体已被提出和实现模拟哈密顿模拟器解决大规模的硬优化问题。然而，在这些realisations的问题的参数取决于节点occupancy是不知道的先验，这限制了增益耗散模拟器的适用性，很容易解决的经典计算类的问题。我们将展示如何克服这一困难，并制定这样的模拟器的操作原则，用于解决NP难的大规模优化问题，如恒模连续二次优化和二次二进制优化的任何一般矩阵。为了解决这样的问题，任何增益耗散模拟器必须实现一个反馈机制的增益和耦合强度的动态调整。
Recently several gain-dissipative platforms based on the networks of optical parametric oscillators, lasers and various non-equilibrium Bose-Einstein condensates have been proposed and realised as analogue Hamiltonian simulators for solving large-scale hard optimisation problems. However, in these realisations the parameters of the problem depend on the node occupancies that are not known a priori, which limits the applicability of the gain-dissipative simulators to the classes of problems easily solvable by classical computations. We show how to overcome this difficulty and formulate the principles of operation of such simulators for solving the NP-hard large-scale optimisation problems such as constant modulus continuous quadratic optimisation and quadratic binary optimisation for any general matrix. To solve such problems any gain-dissipative simulator has to implement a feedback mechanism for the dynamical adjustment of the gain and coupling strengths.