arXiv Machine Learning
Jun 9

Quantum latent distributions in deep generative models

arXiv:2508. 19857v3 Announce Type: replace Abstract: Many successful families of generative models leverage a low-dimensional latent distribution that is mapped to a data distribution.

By Omar Bacarreza, Thorin Farnsworth, Alexander Makarovskiy, Hugo Wallner, Tessa Hicks, Santiago Sempere-Llagostera, John Price, Robert J. A. Francis-Jones, William R. Clements
arXiv Machine Learning
Sep 3

Quantum MeanFlow: single-shot generative sampling on NISQ hardware

Quantum MeanFlow (QMF) is a new quantum generative sampling method that enables single‑step sample generation by learning an average velocity field over a time interval, unlike the multi‑step quantum flow matching (QFM) which requires sequential integration of an ordinary differential equation. Using parameterized quantum circuits, the authors benchmark QMF and QFM on the MNIST dataset, finding that QMF produces lower image quality than multi‑step QFM but outperforms single‑step QFM at every shot count. Both models were executed on IBM quantum computers, and best‑of‑N rejection sampling mitigates device noise without circuit modification, demonstrating QMF’s practicality for efficient single‑step quantum generative sampling.

By Ashish Joshi, Eshaan Mistry, Takahiko Koyama
arXiv Machine Learning
Jun 2

Latent-Conditioned Parameterized Quantum Circuits as Universal Approximators for Distributions over Quantum States

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.

By Quoc Hoan Tran, Koki Chinzei, Yasuhiro Endo, Hirotaka Oshima
arXiv Computer Vision
Sep 30

Quantum Fidelity Landscape-Guided Prior Calibration for Single-Circuit QGAN Image Generation

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.

By Xue Yang, Rigui Zhou, Dax Enshan Koh, Siong Thye Goh, Yitao Tang, ShiZheng Jia, Young-Wook Cho, Hongyu Chen
arXiv AI
Oct 5

Evolving Hybrid Quantum-Classical Architectures for Image Classification

The paper presents an evolutionary framework, EXAQC, that automatically discovers parameterized quantum circuits (PQCs) for use as intermediate modules in hybrid quantum‑classical neural networks for image classification. By evolving PQCs while keeping classical feature‑extraction and prediction layers fixed, the authors achieve high accuracies on MNIST, Fashion‑MNIST, and CIFAR‑10 with gate counts comparable to other quantum architecture‑search methods. The evolved hybrid models match or exceed the performance of classical networks while using far fewer trainable parameters, and the choice of encoding (rotation‑based vs amplitude) significantly impacts accuracy.

By Devroop Kar, Daniel Krutz, Travis Desell