Implementation of Quantum Implicit Neural Representation in Deterministic and Probabilistic Autoencoders for Image Reconstruction/Generation Tasks

arXiv:2603.06755 · cs.LG, quant-ph · Submitted 2026-03-06 · Read on arXiv

cs.LG, quant-ph

Submitted: 2026-03-06

Updated: 2026-09-21

Journal ref: Phys. Scr. 101 (2026) 385102

DOI: 10.1088/1402-4896/aea65e

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Cosine Similarity — 15× in this paper · explained in 3 episodes
• Latent Space — 8× in this paper · explained in 7 episodes
• Generative Model — 7× in this paper · explained in 2 episodes
• Adam — 6× in this paper · explained in 2 episodes
• Generative Models — 6× in this paper · explained in 2 episodes
• Latent Representation — 5× in this paper · explained in 2 episodes
• Loss Function — 5× in this paper · explained in 2 episodes
• Mode Collapse — 5× in this paper · explained in 2 episodes
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Hybrid Models — 3× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Variational Autoencoder (VAE) — 3× in this paper · explained in 3 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Mean Squared Error — 2× in this paper
• Quantum Circuits — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• PennyLane: Automatic differentiation of hybrid quantum-classical computations

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks