Cover Image

Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	de Schoulepnikoff, Paulin, Muñoz-Gil, Gorka, Nautrup, Hendrik Poulsen, Briegel, Hans J.
Format:	Preprint
Published:	2025
Subjects:	Quantum Physics Statistical Mechanics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Discovering quantum phenomena with Interpretable Machine Learning
by: de Schoulepnikoff, Paulin, et al.
Published: (2026)

Disentanglement by means of action-induced representations
by: Muñoz-Gil, Gorka, et al.
Published: (2026)

Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging
by: de Schoulepnikoff, Paulin, et al.
Published: (2023)

Minimizing classical resources in variational measurement-based quantum computation for generative modeling
by: Majumder, Arunava, et al.
Published: (2026)

Measurement-based quantum computation from Clifford quantum cellular automata
by: Nautrup, Hendrik Poulsen, et al.
Published: (2023)

Learning to reset in target search problems
by: Muñoz-Gil, Gorka, et al.
Published: (2025)

Unsupervised learning of anomalous diffusion data
by: Muñoz-Gil, Gorka, et al.
Published: (2021)

Quantum-data-driven dynamical transition in quantum learning
by: Zhang, Bingzhi, et al.
Published: (2024)

Fundamentals of quantum Boltzmann machine learning with visible and hidden units
by: Wilde, Mark M.
Published: (2025)

All you need is spin: SU(2) equivariant variational quantum circuits based on spin networks
by: East, Richard D. P., et al.
Published: (2023)

Optimal foraging strategies can be learned
by: Muñoz-Gil, Gorka, et al.
Published: (2023)

Learning quantum many-body data locally: A provably scalable framework
by: Chinzei, Koki, et al.
Published: (2025)

Learning minimal representations of stochastic processes with variational autoencoders
by: Fernández-Fernández, Gabriel, et al.
Published: (2023)

Dynamical transition in controllable quantum neural networks with large depth
by: Zhang, Bingzhi, et al.
Published: (2023)

SMT-AD: a scalable quantum-inspired anomaly detection approach
by: Sornsaeng, Apimuk, et al.
Published: (2026)

Meta-learning of Gibbs states for many-body Hamiltonians with applications to Quantum Boltzmann Machines
by: Bhat, Ruchira V, et al.
Published: (2025)

Neural-network quantum state study of the long-range antiferromagnetic Ising chain
by: Kim, Jicheol, et al.
Published: (2023)

Fermi-Dirac thermal measurements: A framework for quantum hypothesis testing and semidefinite optimization
by: Liu, Nana, et al.
Published: (2026)

Filtering out mislabeled training instances using black-box optimization and quantum annealing
by: Otsuka, Makoto, et al.
Published: (2025)

Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations
by: Egenlauf, Patrick, et al.
Published: (2025)

Learning Minimal Representations of Many-Body Physics from Snapshots of a Quantum Simulator
by: Møller, Frederik, et al.
Published: (2025)

Tensor tree learns hidden relational structures in data to construct generative models
by: Harada, Kenji, et al.
Published: (2024)

Transport and information in open quantum systems
by: Poulsen, Kasper
Published: (2024)

Learning to erase quantum states: thermodynamic implications of quantum learning theory
by: Zhao, Haimeng, et al.
Published: (2025)

Variational measurement-based quantum computation for generative modeling
by: Majumder, Arunava, et al.
Published: (2023)

Variational autoencoders understand knot topology
by: Braghetto, Anna, et al.
Published: (2025)

Quantum circuit synthesis with diffusion models
by: Fürrutter, Florian, et al.
Published: (2023)

The Work Capacity of Channels with Memory: Maximum Extractable Work in Percept-Action Loops
by: Fiderer, Lukas J., et al.
Published: (2025)

Quantum Boltzmann machine learning of ground-state energies
by: Patel, Dhrumil, et al.
Published: (2024)

Natural gradient and parameter estimation for quantum Boltzmann machines
by: Patel, Dhrumil, et al.
Published: (2024)

Interpretable machine learning of amino acid patterns in proteins: a statistical ensemble approach
by: Braghetto, Anna, et al.
Published: (2023)

Heat-based circuits using quantum rectification
by: Poulsen, Kasper, et al.
Published: (2022)

Effectiveness of Binary Autoencoders for QUBO-Based Optimization Problems
by: Abe, Tetsuro, et al.
Published: (2026)

Generative Learning of Continuous Data by Tensor Networks
by: Meiburg, Alex, et al.
Published: (2023)

Quantum Inception Score
by: Sone, Akira, et al.
Published: (2023)

Local Diffusion Models and Phases of Data Distributions
by: Hu, Fangjun, et al.
Published: (2025)

Multi-Mode Quantum Annealing for Generative Representation Learning with Boltzmann Priors
by: Kim, Gilhan, et al.
Published: (2026)

Quantum consistent neural/tensor networks for photonic circuits with strongly/weakly entangled states
by: Allegra, Nicolas
Published: (2024)

Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics
by: Casagrande, Heitor P., et al.
Published: (2024)

Classical Shadows with Improved Median-of-Means Estimation
by: Fu, Winston, et al.
Published: (2024)