QuantumGS: Quantum Encoding Framework for Gaussian Splatting

2026-02-04

AI Summary

This work addresses the limitations of traditional 3D Gaussian splatting in accurately modeling high-frequency view-dependent effects—such as sharp reflections and transparency—and the restricted representational capacity of existing MLP-based hybrid approaches under low parameter budgets. To overcome these challenges, we propose the first integration of variational quantum circuits (VQCs) into the Gaussian splatting framework. Our method encodes viewing directions directly onto the Bloch sphere and replaces the classical color modulation network with a quantum circuit. By leveraging the intrinsic geometric properties of qubits to efficiently represent 3D directional information, our approach achieves superior modeling accuracy and generalization for high-frequency view-dependent appearance, while significantly reducing the number of parameters. This results in higher-quality reconstructions than classical MLP-based methods, all within real-time rendering constraints.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: 3D Computer Vision
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recent advances in neural rendering, particularly 3D Gaussian Splatting (3DGS), have enabled real-time rendering of complex scenes. However, standard 3DGS relies on spherical harmonics, which often struggle to accurately capture high-frequency view-dependent effects such as sharp reflections and transparency. While hybrid approaches like Viewing Direction Gaussian Splatting (VDGS) mitigate this limitation using classical Multi-Layer Perceptrons (MLPs), they remain limited by the expressivity of classical networks in low-parameter regimes. In this paper, we introduce QuantumGS, a novel hybrid framework that integrates Variational Quantum Circuits (VQC) into the Gaussian Splatting pipeline. We propose a unique encoding strategy that maps the viewing direction directly onto the Bloch sphere, leveraging the natural geometry of qubits to represent 3D directional data. By replacing classical color-modulating networks with quantum circuits generated via a hypernetwork or conditioning mechanism, we achieve higher expressivity and better generalization. Source code is available in the supplementary material. Code is available at https://github.com/gwilczynski95/QuantumGS

Problem

• 3D Gaussian Splatting
• view-dependent effects
• high-frequency details
• expressivity
• neural rendering

Innovation

• QuantumGS
• Variational Quantum Circuits
• Gaussian Splatting
• Bloch Sphere Encoding
• Neural Rendering