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Accelerating recurrent Ising machines in photonic integrated circuits
Optica ( IF 10.4 ) Pub Date : 2020-05-18 , DOI: 10.1364/optica.386613
Mihika Prabhu , Charles Roques-Carmes , Yichen Shen , Nicholas Harris , Li Jing , Jacques Carolan , Ryan Hamerly , Tom Baehr-Jones , Michael Hochberg , Vladimir Čeperić , John D. Joannopoulos , Dirk R. Englund , Marin Soljačić

Conventional computing architectures have no known efficient algorithms for combinatorial optimization tasks such as the Ising problem, which requires finding the ground state spin configuration of an arbitrary Ising graph. Physical Ising machines have recently been developed as an alternative to conventional exact and heuristic solvers; however, these machines typically suffer from decreased ground state convergence probability or universality for high edge-density graphs or arbitrary graph weights, respectively. We experimentally demonstrate a proof-of-principle integrated nanophotonic recurrent Ising sampler (INPRIS), using a hybrid scheme combining electronics and silicon-on-insulator photonics, that is capable of converging to the ground state of various four-spin graphs with high probability. The INPRIS results indicate that noise may be used as a resource to speed up the ground state search and to explore larger regions of the phase space, thus allowing one to probe noise-dependent physical observables. Since the recurrent photonic transformation that our machine imparts is a fixed function of the graph problem and therefore compatible with optoelectronic architectures that support GHz clock rates (such as passive or non-volatile photonic circuits that do not require reprogramming at each iteration), this work suggests the potential for future systems that could achieve orders-of-magnitude speedups in exploring the solution space of combinatorially hard problems.

中文翻译:

加速光子集成电路中的Ising循环机

传统的计算体系结构没有用于组合优化任务(例如Ising问题)的有效算法,该算法需要找到任意Ising图的基态自旋配置。最近开发了物理Ising机器,以替代传统的精确和启发式求解器。但是,对于高边沿密度图或任意图权重,这些机器通常遭受降低的基态收敛概率或通用性的困扰。我们通过结合电子和绝缘体上硅光子学的混合方案,通过实验证明了原理验证的集成纳米光子循环伊辛采样器(INPRIS),它能够以高概率收敛到各种四轴图的基态。INPRIS结果表明,噪声可以用作加快基态搜索和探索相空间较大区域的资源,从而使人们可以探究噪声相关的物理观测值。由于我们的机器提供的递归光子转换是图形问题的固定函数,因此与支持GHz时钟速率的光电体系结构兼容(例如不需要在每次迭代中重新编程的无源或非易失性光子电路),因此这项工作建议在探索组合难题的解决空间时,未来系统可能实现数量级加速。
更新日期:2020-05-18
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