Learning Disentangled Representations with Quantum Variational Autoencoders

2026-10-05

AI Summary

This study addresses the ambiguous definition and limited interpretability of discrete latent dimensions in Quantum Variational Autoencoders (QVAEs). To overcome the high-dimensional complexity of Hilbert space, this work proposes a mechanism that designates individual qubits as independent semantic factors. By introducing a quantum regularization strategy and combining theoretical analysis with experiments on synthetic datasets such as MNIST, we systematically investigate the composition of quantum latent dimensions and their factor disentanglement capabilities. Our findings demonstrate that QVAEs can effectively discover interpretable, factorized representations, thereby establishing both theoretical and empirical foundations for structured quantum representation learning.

Abstract

Variational autoencoders are powerful representation learning models that map complex data into low-dimensional latent spaces, enabling the discovery of interpretable and disentangled factors. Such representations can facilitate the interpretation and controllable generation of data describing complex scientific systems. Understanding how these factors are organized and encoded in latent space is therefore important for developing reliable representation learning models. Recently, quantum variational autoencoders (QVAEs) have been proposed as quantum representation models, demonstrating informative latent representations and improved latent-space occupancy through quantum regularization. However, it remains unclear whether and how QVAEs can learn disentangled and interpretable latent factors. A key challenge in investigating quantum latent factors is that a small number of qubits spans an exponentially large Hilbert space, making the notion of an individual quantum latent dimension nontrivial. Here, we investigate what constitutes an individual quantum latent dimension and whether it can encode a distinct factor. We develop theoretical insights into quantum latent dimensions and support them with empirical studies on representative synthetic problems, including MNIST variants. Across three datasets, we demonstrate that QVAEs can discover factorized and semantically interpretable latent representations, with individual qubits functioning as meaningful latent factors. These results establish a foundation for understanding quantum latent spaces and their potential for structured and interpretable representation learning.

Problem

• Quantum Variational Autoencoders
• Disentangled Representations
• Quantum Latent Space
• Interpretable Factors

Innovation

• Quantum Variational Autoencoders
• Disentangled Representations
• Quantum Latent Space
• Representation Learning
• Interpretability