analiza aiqbench
This research investigates whether quantum variational autoencoders (QVAEs) can successfully learn disentangled and interpretable latent factors, addressing a fundamental ambiguity in how individual qubits encode distinct information within an exponentially large Hilbert space. Through theoretical analysis and empirical studies on synthetic datasets like MNIST variants, the authors demonstrate that QVAEs can indeed discover factorized representations where specific qubits function as meaningful latent dimensions. These findings provide a crucial foundation for leveraging quantum representation learning to interpret and generate data for complex scientific systems.
Learning Disentangled Representations with Quantum Variational Autoencoders
arXiv:2610.07196v1 Announce Type: cross 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.
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.
Quantum Physics (quant-ph); Emerging Technologies (cs.ET); Machine Learning (cs.LG)
arXiv:2610.07196 [quant-ph]
(or arXiv:2610.07196v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2610.07196 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 308 cuvinte
Subiecte conexe
Feature Reduction Improves Quantum Extreme Learning Machines
Quantum Zeitgeist · 24 iul. 2026
Kaist Team Develops Variational Autoencoder Framework for Quantum Data Embedding
Quantum Zeitgeist · 16 iul. 2026
Disentanglement with Holographic Reduced Representations
arXiv · 9 iun. 2026
Kankeu and Colleagues Present Hybrid Variational Autoencoder for Neural Topic Modeling
Quantum Zeitgeist · 17 iun. 2026