具有增益耗散系统的自旋哈密顿量的全局优化
Global optimization of spin Hamiltonians with gain-dissipative systems
摘要：
最近，提出了几个平台，并证明了使用增益耗散量子和经典系统找到自旋哈密顿量全局最小值的原理证明，例如Ising和XY模型。增益和耦合强度的动态调节的实现已经被建立为旨在模拟自旋哈密顿的模拟哈密顿物理系统的重要反馈机制。基于这样的模拟器的操作原理，我们开发了一类新的增益耗散算法的NP-难问题的全局优化，并显示其性能与经典的全局优化算法相比。这些系统可以用来研究自旋系统的基态和统计特性，并作为增益耗散物理模拟器性能测试的直接基准。估计的时间操作的物理实现的增益耗散模拟器的大矩阵显示了可能的速度的几个数量级相比，经典的计算。
英文摘要：
Recently, several platforms were proposed and demonstrated a proof-of-principle for finding the global minimum of the spin Hamiltonians such as the Ising and XY models using gain-dissipative quantum and classical systems. The implementation of dynamical adjustment of the gain and coupling strengths has been established as a vital feedback mechanism for analog Hamiltonian physical systems that aim to simulate spin Hamiltonians. Based on the principle of operation of such simulators we develop a novel class of gain-dissipative algorithms for global optimisation of NP-hard problems and show its performance in comparison with the classical global optimisation algorithms. These systems can be used to study the ground state and statistical properties of spin systems and as a direct benchmark for the performance testing of the gain-dissipative physical simulators. The estimates of the time operation of the physical implementation of the gain-dissipative simulators for large matrices show a possible speed-up of the several orders of magnitude in comparison with classical computations.
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。
