Transparent radiative coolers can be used as window materials to reduce cooling energy needs for buildings and automobiles, which may contribute significantly to addressing climate change challenges. However, it is difficult to achieve high visible transparency and radiative cooling performance simultaneously. Here, we design a visually transparent radiative cooler on the basis of layered photonic structures using a quantum computing-assisted active learning scheme, which combines active data production, machine learning, and quantum annealing in an iterative loop. We experimentally fabricate the designed cooler and demonstrate its cooling effect. This cooler may lead to an annual energy saving of up to 86.3 MJ/m² in hot climates compared with normal glass windows. The quantum annealing-assisted active learning scheme may be generalized for the design of other complex materials.

中文翻译：

---

量子计算设计的高性能透明辐射冷却器

透明辐射冷却器可用作窗户材料，以减少建筑物和汽车的冷却能源需求，这可能对应对气候变化挑战做出重大贡献。然而，很难同时实现高可见光透明度和辐射冷却性能。在这里，我们使用量子计算辅助主动学习方案在分层光子结构的基础上设计了一个视觉透明的辐射冷却器，该方案将主动数据生成、机器学习和量子退火结合在一个迭代循环中。我们通过实验制造了设计的冷却器并展示了其冷却效果。该冷却器每年可节省高达 86.3 MJ/m ^2的能源与普通玻璃窗相比，在炎热的气候下。量子退火辅助主动学习方案可以推广到其他复杂材料的设计。