Recently, Quantum Visual Fields (QVFs) have shown promising improvements in model compactness and convergence speed for learning the provided 2D or 3D signals. Meanwhile, novel-view synthesis has seen major advances with Neural Radiance Fields (NeRFs), where models learn a compact representation from 2D images to render 3D scenes, albeit at the cost of larger models and intensive training. In this work, we extend the approach of QVFs by introducing QNeRF, the first hybrid quantum-classical model designed for novel-view synthesis from 2D images. QNeRF leverages parameterised quantum circuits to encode spatial and view-dependent information via quantum superposition and entanglement, resulting in more compact models compared to the classical counterpart. We present two architectural variants. Full QNeRF maximally exploits all quantum amplitudes to enhance representational capabilities. In contrast, Dual-Branch QNeRF introduces a task-informed inductive bias by branching spatial and view-dependent quantum state preparations, drastically reducing the complexity of this operation and ensuring scalability and potential hardware compatibility. Our experiments demonstrate that -- when trained on images of moderate resolution -- QNeRF matches or outperforms classical NeRF baselines while using less than half the number of parameters. These results suggest that quantum machine learning can serve as a competitive alternative for continuous signal representation in mid-level tasks in computer vision, such as 3D representation learning from 2D observations.

翻译：近年来，量子视觉场（QVFs）在学习给定二维或三维信号方面，已在模型紧凑性和收敛速度上展现出有前景的改进。与此同时，神经辐射场（NeRFs）在新型视图合成领域取得了重大进展，该模型通过从二维图像中学习紧凑表示来渲染三维场景，但其代价是模型规模更大且训练强度更高。在本工作中，我们通过引入QNeRF扩展了QVFs的方法，QNeRF是首个为从二维图像进行新型视图合成而设计的混合量子-经典模型。QNeRF利用参数化量子电路，通过量子叠加和纠缠对空间和视角相关信息进行编码，从而相比经典对应模型实现了更紧凑的表示。我们提出了两种架构变体。完整版QNeRF充分利用所有量子振幅以增强表示能力。相比之下，双分支QNeRF通过分支化空间和视角相关的量子态制备引入任务导向的归纳偏置，从而大幅降低了该操作的复杂度，并确保了可扩展性和潜在的硬件兼容性。我们的实验表明——在中等分辨率图像上训练时——QNeRF在仅使用不到一半参数量的情况下，即可达到或超越经典NeRF基线模型的性能。这些结果表明，量子机器学习可作为计算机视觉中层级任务的连续信号表示（例如从二维观测中学习三维表示）的竞争性替代方案。