Towards optimization of photonic-crystal surface-emitting lasers via quantum annealing
Towards optimization of photonic-crystal surface-emitting lasers via quantum annealing
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DOI：
10.1364/oe.476839
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发表时间：
2022-11-21
期刊：
影响因子：
3.8
通讯作者：
Noda, Susumu
中科院分区：
文献类型：
--
作者：
Inoue, Takuya;Seki, Yuya;Noda, Susumu
光子晶体表面发射激光器（PCSELs）利用光子晶体内部的二维（2D）光学共振进行激光，具有单模高功率操作和光束偏振任意控制等多种突出功能。虽然以往的设计大多采用空间均匀的光子晶体，但如果能够优化光子晶体的空间分布，有望进一步提高激光性能。在本文中，我们研究了利用量子退火的方法来优化PCSELs的结构，使其具有高功率、窄光束发散的线性偏振工作。通过以下三个步骤进行优化：(1)随时间变化的三维耦合波激光性能分析，(2)通过因式分解机计算激光性能，(3)通过量子退火选择最优解。通过这种方法，我们发现了一种具有非均匀带边频率和注入电流空间分布的先进PCSEL，同时具有更高的输出功率，更窄的发散角和更高的线性极化比。我们的研究结果潜在地表明量子退火的普遍适用性，到目前为止，量子退火主要应用于特定类型的离散优化问题，用于智能制造领域的各种物理和工程问题。(C)根据Optica开放获取出版协议条款，2022年Optica出版集团
Photonic-crystal surface-emitting lasers (PCSELs), which utilize a two-dimensional (2D) optical resonance inside a photonic crystal for lasing, feature various outstanding functionalities such as single-mode high-power operation and arbitrary control of beam polarizations. Although most of the previous designs of PCSELs employ spatially uniform photonic crystals, it is expected that lasing performance can be further improved if it becomes possible to optimize the spatial distribution of photonic crystals. In this paper, we investigate the structural optimization of PCSELs via quantum annealing towards high-power, narrow-beam-divergence operation with linear polarization. The optimization of PCSELs is performed by the iteration of the following three steps: (1) time-dependent 3D coupled-wave analysis of lasing performance, (2) formulation of the lasing performance via a factorization machine, and (3) selection of optimal solution(s) via quantum annealing. By using this approach, we discover an advanced PCSEL with a non-uniform spatial distribution of the band-edge frequency and injection current, which simultaneously enables higher output power, a narrower divergence angle, and a higher linear polarization ratio than conventional uniform PCSELs. Our results potentially indicate the universal applicability of quantum annealing, which has been mainly applied to specific types of discrete optimization problems so far, for various physics and engineering problems in the field of smart manufacturing. (C) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement