Solving complex optimization problems with a coherent Ising machine

H. Takesue, T. Inagaki, T. Honjo
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Abstract

As the various systems in our society grow larger and more complex, their analysis and optimization grow increasingly important. Many such tasks are classified as combinatorial optimization problems, which can be mapped onto the ground-statesearch problems of the Ising model.1 Recently, several approaches to simulating the Ising model have been demonstrated using artificial spin networks, such as superconducting quantum bits (qubits)2 and CMOS devices.3 These physical Ising machines have suffered from a limited number of spin-spin couplings, however, because of the use of solid-state devices as artificial spins. We have realized a coherent Ising machine (i.e., an artificial spin network based on quantum electronics technologies, CIM) that is instead based on photonics.4 To achieve this, we used time-multiplexed degenerate optical parametric oscillators (DOPOs)5, 6 as artificial spins, and realized all-to-all coupling between 2048 DOPOs using a measurement-feedback scheme.7 We experimentally confirmed that our CIM can find solutions for NP-hard maximum cut problems of a 2000-node complete graph.4 The setup of our CIM is illustrated in Figure 1. A periodically poled lithium niobate (PPLN) waveguide module is placed in a fiber ring cavity, which includes a 1km fiber delay line, an optical bandpass filter, optical couplers, and a fiber stretcher for cavity-phase stabilization. When we inject pump pulses with a wavelength of p into the PPLN waveguide, pulsed spontaneous emission noise begins circulating in the cavity. If we limit the wavelength component to 2 p using the optical bandpass filter, parametric amplification occurs only at signal-idler degeneracy, i.e., where only lights with 0 or phase components Figure 1. The setup of our coherent Ising machine (CIM). The optical bandpass filter and fiber stretcher in the cavity are not shown for conciseness. FPGA: Field-programmable gate array. OPO: Optical parametric oscillator. PPLN: Periodically poled lithium niobate.
用相干伊辛机求解复杂优化问题
随着我们社会中的各种系统变得越来越大,越来越复杂,它们的分析和优化变得越来越重要。许多这样的任务被归类为组合优化问题,这可以映射到伊辛模型的地面状态研究问题1最近,几种模拟伊辛模型的方法已经被证明使用人工自旋网络,如超导量子比特(qubits)2和CMOS器件然而,由于使用固态器件作为人工自旋,这些物理伊辛机器的自旋耦合数量有限。我们已经实现了一个相干伊辛机(即基于量子电子技术的人工自旋网络,CIM),而不是基于光子学为了实现这一目标,我们使用时间复用简并光参量振荡器(dopo) 5,6作为人工自旋,并使用测量反馈方案实现了2048个dopo之间的全对全耦合我们通过实验证实,我们的CIM可以找到2000个节点完整图的NP-hard最大割问题的解我们的CIM的设置如图1所示。周期性极化铌酸锂(PPLN)波导模块被放置在光纤环形腔中,该环形腔包括1km光纤延迟线、光带通滤波器、光耦合器和用于腔相位稳定的光纤拉伸器。当我们向PPLN波导注入波长为p的泵浦脉冲时,脉冲自发发射噪声开始在腔内循环。如果我们使用光学带通滤波器将波长分量限制为2 p,则参数放大仅发生在信号空闲简并处,即只有具有0或相位分量的光(图1)。我们的相干Ising机器(CIM)的设置。为简洁起见,在腔内不示出光学带通滤波器和光纤拉伸器。FPGA:现场可编程门阵列。OPO:光参量振荡器PPLN:周期性极化铌酸锂。
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