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 AI
Aug 13

CoQui: A Coordinate-Conditioned Quantum Implicit Generative Adversarial Network for End-to-End Image Generation

arXiv:2608. 11884v1 Announce Type: cross Abstract: Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits.

By Xue Yang, Rigui Zhou, ShiZheng Jia, Dax Enshan Koh, Siong Thye Goh, Young-Wook Cho, YaoChong Li, Xuezhi Ma, Hongyu Chen, Xin Wang
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