An Ising Hamiltonian solver based on coupled stochastic phase-transition nano-oscillators
An Ising Hamiltonian solver based on coupled stochastic phase-transition nano-oscillators
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DOI：
10.1038/s41928-021-00616-7
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发表时间：
2021-07-01
影响因子：
34.3
通讯作者：
Datta, S.
中科院分区：
文献类型：
--
作者：
Dutta, S.;Khanna, A.;Datta, S.
组合优化问题属于非确定性多项式时间（NP） - hard复杂度类别及其计算需求类别，并以问题大小为指数级。可以将它们映射到找到Ising模型的基态的问题中，该模型描述了具有收敛动力学的物理系统。已经探索了各种平台，包括光学，电子和量子方法，以加速地面搜索，但是仍然需要提高能源效率和计算能力。在这里，我们报告了基于形成连续时间动力系统（CTDS）的电相相变纳米振荡器（PTNOS）网络的ISINS求解器。注射锁的PTNOS的双稳定阶段充当人工式自旋，CTD的稳定点充当问题的基态解决方案。我们从实验上表明，具有八个PTNO的原型可以解决成功的NP巨大问题，并具有很高的成功概率（600个退火周期为96％）。 We also show via numerical simulations that our Ising Hamiltonian solver can solve MaxCut problems of 100 nodes with energy efficiency of 1.3 x 10(7) solutions per second per watt, offering advantages over other approaches including memristor-based Hopfield networks, quantum annealers and photonic Ising solvers.An Ising solver that is based on a network of electrically coupled phase-transition nano-oscillators, which provides a连续时间动态系统可用于有效解决非确定性多项式时间（NP） - hard maxcut问题。
Combinatorial optimization problems belong to the non-deterministic polynomial time (NP)-hard complexity class, and their computational requirements scale exponentially with problem size. They can be mapped into the problem of finding the ground state of an Ising model, which describes a physical system with converging dynamics. Various platforms, including optical, electronic and quantum approaches, have been explored to accelerate the ground-state search, but improvements in energy efficiencies and computational abilities are still required. Here we report an Ising solver based on a network of electrically coupled phase-transition nano-oscillators (PTNOs) that form a continuous-time dynamical system (CTDS). The bi-stable phases of the injection-locked PTNOs act as artificial Ising spins and the stable points of the CTDS act as the ground-state solution of the problem. We experimentally show that a prototype with eight PTNOs can solve an NP-hard MaxCut problem with high probability of success (96% for 600 annealing cycles). We also show via numerical simulations that our Ising Hamiltonian solver can solve MaxCut problems of 100 nodes with energy efficiency of 1.3 x 10(7) solutions per second per watt, offering advantages over other approaches including memristor-based Hopfield networks, quantum annealers and photonic Ising solvers.An Ising solver that is based on a network of electrically coupled phase-transition nano-oscillators, which provides a continuous-time dynamical system, can be used to efficiently solve a non-deterministic polynomial time (NP)-hard MaxCut problem.