Quantum-JEPA: Quantum Born Priors for Latent-Action JEPA World Models
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
A single observation can admit several plausible futures, making uncertainty an essential part of world modelling. However, quantum generative models remain underexplored for this task. We introduce Q...
arXiv:2605. 28690v2 Announce Type: replace-cross Abstract: Many applications in quantum simulation, quantum chemistry, and quantum machine learning require not a single quantum state but an ensemble of states characterizing the heterogeneity of a target system.
arXiv:2608. 11884v1 Announce Type: cross Abstract: Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits.
Quantum Fidelity Landscape-Guided Prior Calibration for Single-Circuit QGAN Image Generation explores a simpler pixel‑level, end‑to‑end approach to quantum generative adversarial networks (QGANs) that avoids patch‑based decomposition. The authors introduce the Quantum Fidelity Landscape (QFL) as an invariant pairwise‑fidelity structure preserved under shared unitary transformations, and use it to calibrate the quantum prior before adversarial training. Their BasicQGAN framework aligns the prior‑induced QFL with the data‑induced QFL, achieving stable, effective image generation on small‑scale grayscale datasets while requiring fewer qubits and trainable parameters than patch‑based quantum generators.
Learning latent dynamics is fundamental to building action-conditioned world models for planning. Among existing families, Joint Embedding Predictive Architectures (JEPAs) offer a natural framework by...
arXiv:2603.06755v3 Announce Type: replace Abstract: We propose a quantum implicit neural representation (QINR)-based autoencoder (AE) and variational autoencoder (VAE) for image reconstruction and ge...