Quantum implicit neural representations for 3D scene reconstruction and novel view synthesis

Abstract Implicit neural representations (INRs) have become a powerful paradigm for continuous signal modeling and 3D scene reconstruction, yet classical networks suffer from a well-known spectral bias that limits their ability to capture high-frequency details. Quantum Implicit Representation Networks (QIREN) mitigate this limitation by employing parameterized quantum circuits with inherent Fourier structures, enabling compact and expressive frequency modeling beyond classical MLPs. In this paper, we present Quantum Neural Radiance Fields (Q-NeRF), a hybrid quantum–classical framework that offers a first proof-of-concept bridge between QIREN and neural radiance field rendering. Q-NeRF integrates QIREN modules into the Nerfacto backbone, preserving its efficient sampling, pose refinement, and volumetric rendering strategies while replacing selected density and radiance prediction components with quantum-enhanced counterparts. We systematically evaluate three hybrid configurations on a single controlled, low-resolution multi-view indoor scene, comparing them to classical baselines using PSNR, SSIM, and LPIPS metrics. Results show that hybrid quantum-classical models achieve competitive reconstruction quality on the scene considered under limited computational resources, with quantum modules most useful for fine-scale, view-dependent appearance; we do not, however, claim a broad improvement in 3D reconstruction. Although current implementations rely on quantum circuit simulators constrained to few-qubit regimes, the results highlight the potential of quantum encodings to alleviate spectral bias in implicit representations. Q-NeRF is intended as an exploratory first step and a baseline for future quantum neural rendering research, rather than as evidence of a practical quantum advantage in 3D scene reconstruction.