Quantum Implicit Neural Representations for 3D Scene Reconstruction and Novel View Synthesis

TL;DR

This paper introduces a hybrid quantum-classical framework called Q-NeRF that enhances 3D scene reconstruction and view synthesis by leveraging quantum circuits to better capture high-frequency details, demonstrating promising results on standard datasets.

Contribution

The paper presents the first hybrid quantum-classical neural radiance field framework, integrating quantum modules into existing models to improve high-frequency detail representation in 3D reconstruction.

Findings

Quantum modules effectively capture fine-scale, view-dependent appearance.

Hybrid models achieve competitive quality with limited computational resources.

Quantum encodings can mitigate spectral bias in implicit neural representations.

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), the first hybrid quantum-classical framework for 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 standard multi-view indoor datasets, comparing them to classical baselines using PSNR, SSIM, and LPIPS metrics. Results show that hybrid quantum-classical models achieve competitive reconstruction quality under limited computational resources, with quantum modules particularly effective in representing fine-scale, view-dependent appearance. 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 provides a foundational step toward scalable quantum-enabled 3D scene reconstruction and a baseline for future quantum neural rendering research.