Multi-band spectral filters that can transmit visible light but block UV and infrared light in the solar spectrum are applicable to energy-saving windows. However, such filters are usually designed to consider normal incident light only. Here, we report photonic structures allowing selective solar spectrum transmission in wide angles using a quantum-computing-enhanced active learning scheme, which includes machine learning, quantum annealing, and wave-optics simulation in an iterative loop. We experimentally demonstrate the optical characteristics of the photonic structure and its capability to reduce the temperature rise in an enclosure when combined with a thermal radiation layer (temperature reduction of 5.4°C–7.2°C and annual energy saving of ∼97.5 MJ/m). This structure can be incorporated into existing windows in buildings or automobiles to reduce cooling energy consumption, and the active learning scheme can be applied to design materials with complex properties in general.

中文翻译：

---

量子退火增强主动学习设计的节能窗广角光谱滤光片

可以透射可见光但阻挡太阳光谱中的紫外线和红外线的多波段光谱滤光片适用于节能窗。然而，此类滤光片通常设计为仅考虑法向入射光。在这里，我们报告了使用量子计算增强主动学习方案允许选择性太阳光谱在广角传输的光子结构，其中包括迭代循环中的机器学习、量子退火和波动光学模拟。我们通过实验证明了光子结构的光学特性及其与热辐射层结合时降低外壳温升的能力（温度降低5.4°C–7.2°C，年节能约97.5 MJ/m） 。这种结构可以合并到建筑物或汽车的现有窗户中，以减少冷却能耗，并且主动学习方案通常可以应用于设计具有复杂属性的材料。