# A Quantum-Powered Photorealistic Rendering

arXiv: https://arxiv.org/abs/2211.03418 · HTML: https://mlsanity.com/paper/2211.03418/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 42 references that are in the map.
- First posted on arXiv 7 Nov 2022.

## Builds on (most cited first, 25 of 25)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations](https://mlsanity.com/paper/1906.01618/) (2019)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021)
- [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs](https://mlsanity.com/paper/2103.13744/) (2021)
- [FastNeRF: High-Fidelity Neural Rendering at 200FPS](https://mlsanity.com/paper/2103.10380/) (2021)
- [Learned Initializations for Optimizing Coordinate-Based Neural Representations](https://mlsanity.com/paper/2012.02189/) (2020)
- [DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks](https://mlsanity.com/paper/2103.03231/) (2021)
- [Texture Fields: Learning Texture Representations in Function Space](https://mlsanity.com/paper/1905.07259/) (2019)
- [AutoInt: Automatic Integration for Fast Neural Volume Rendering](https://mlsanity.com/paper/2012.01714/) (2020)
- [Supervised quantum machine learning models are kernel methods](https://mlsanity.com/paper/2101.11020/) (2021)
- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)
- [Robust data encodings for quantum classifiers](https://mlsanity.com/paper/2003.01695/) (2020)
- [Learning Shape Priors for Single-View 3D Completion and Reconstruction](https://mlsanity.com/paper/1809.05068/) (2018)
- [PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report](https://mlsanity.com/paper/1810.01641/) (2018)
- [3D Shape Segmentation via Shape Fully Convolutional Networks](https://mlsanity.com/paper/1702.08675/) (2017)
- [Quantum linear systems algorithms: a primer](https://mlsanity.com/paper/1802.08227/) (2018)
- [QReLU and m-QReLU: Two novel quantum activation functions to aid medical diagnostics](https://mlsanity.com/paper/2010.08031/) (2020)
- [Quantum classification of the MNIST dataset with Slow Feature Analysis](https://mlsanity.com/paper/1805.08837/) (2018)

## Abstract (arXiv)

Achieving photorealistic rendering of real-world scenes poses a significant challenge with diverse applications, including mixed reality and virtual reality. Neural networks, extensively explored in solving differential equations, have previously been introduced as implicit representations for photorealistic rendering. However, achieving realism through traditional computing methods is arduous due to the time-consuming optical ray tracing, as it necessitates extensive numerical integration of color, transparency, and opacity values for each sampling point during the rendering process. In this paper, we introduce Quantum Radiance Fields (QRF), which incorporate quantum circuits, quantum activation functions, and quantum volume rendering to represent scenes implicitly. Our results demonstrate that QRF effectively confronts the computational challenges associated with extensive numerical integration by harnessing the parallelism capabilities of quantum computing. Furthermore, current neural networks struggle with capturing fine signal details and accurately modeling high-frequency information and higher-order derivatives. Quantum computing's higher order of nonlinearity provides a distinct advantage in this context. Consequently, QRF leverages two key strengths of quantum computing: highly non-linear processing and extensive parallelism, making it a potent tool for achieving photorealistic rendering of real-world scenes.