Hash-QNeRF: Multiresolution Hash Encoding for Quantum Neural Radiance Fields

Digonto Biswas; Tana Ballove; Anjan Bandyopadhyay; Sutanu Mangal; Arun Kumar Pati

quant-phJul 23, 2026

Hash-QNeRF: Multiresolution Hash Encoding for Quantum Neural Radiance Fields

Authors: Digonto Biswas, Tana Ballove, Anjan Bandyopadhyay, Sutanu Mangal, Arun Kumar Pati

Abstract

Neural Radiance Fields (NeRF) have revolutionized novel view synthesis, yet their classical implementations remain computationally intensive for high-fidelity rendering. QNeRF recently demonstrated the feasibility of training NeRF on gate-based quantum computers by combining amplitude embedding, parameterized quantum circuits (PQCs), parity-based measurements, and volumetric rendering. However, QNeRF relies on classical sinusoidal positional encoding for spatial coordinates, which scales poorly with scene complexity and resolution. In this work, we replace the sinusoidal positional encoding for spatial coordinates with the multiresolution hash encoding from Instant-NGP while keeping the view-direction encoding, amplitude MLP, quantum circuit, parity measurement, output scaling, and volumetric rendering pipeline unchanged. This hybrid design, Hash-QNeRF, retains the quantum radiance prediction step while benefiting from the fast convergence and memory efficiency of learnable hash grids. On a synthetic Blender scene, we achieve a final training loss of 0.003534, corresponding to approximately 24.5 dB PSNR on the fitted batch. Noise resilience experiments using Qiskit FakeKyiv and FakeTorino backends yield state fidelities of 0.93 to 0.98, indicating that hash encoding does not degrade the quantum circuit's noise tolerance.

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Jan 8, 2026 · cs.CVJ/K move · Enter open · S save

Quantum Implicit Neural Representations for Novel View Synthesis

Daniele Lizzio Bosco, Shuteng Wang, Giuseppe Serra, Vladislav Golyanik

Department of Mathematics, Computer Science and Physics, University of Udine, Italy · Department of Biology, University of Naples Federico II, Italy · work done during an internship at the 4DQV research group, MPI for Informatics · Max Planck Institute for Informatics, SIC, Saarbrücken, Germany

Quantum implicit neural representations have recently emerged as compact function approximators for continuous visual signals. In parallel, Neural Radiance Fields (NeRFs) have become a dominant framework for novel-view synthesis by recovering continuous volumetric scene representations from posed 2D images, but often require substantial model capacity and intensive optimisation. We introduce 3D Quantum Implicit Scene Representation (3D-QISR), the first hybrid quantum-classical radiance-field model for novel-view synthesis from 2D observations. 3D-QISR replaces the classical NeRF backbone with parameterised quantum circuits that encode spatial and view-dependent information through quantum state transformations, enabling compact quantum-enhanced implicit representations while preserving compatibility with standard volumetric rendering. We propose two architectures: Full 3D-QISR, which maximises the representational role of a unified quantum state, and Dual-Branch 3D-QISR, which separates spatial and view-dependent quantum embeddings to introduce a task-aligned inductive bias, reduce state-preparation complexity, and improve scalability and hardware compatibility. Experiments on moderate-resolution novel-view synthesis benchmarks show that 3D-QISR executed on simulated quantum hardware matches or outperforms classical baselines while using fewer than half the trainable parameters. These results position quantum neural networks as compact and competitive building blocks for continuous volumetric scene representation, opening a new direction for quantum-enhanced implicit neural rendering.

Neural Rendering Novel View Synthesis Implicit Neural Representations 3D Scene Understanding

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Abstract

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