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.
arXiv:2608. 11884v1 Announce Type: cross Abstract: Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits.
Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits. Existing amplitude-based approaches face two key limitations: pixel locations are typically encoded by computational-basis indices or address qubits, causing quantum resources to grow with image resolution; meanwhile, jointly decoding many pixels from normalized quantum states introduces probability competition among pixels and limits precise pixel-wise control.
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...
arXiv:2603. 00233v2 Announce Type: replace-cross Abstract: Quantum generative modeling is a rapidly evolving discipline at the intersection of quantum computing and machine learning.
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.
Medical image classification is often constrained by limited labeled data, motivating generative augmentation; recently, quantum generative models have been proposed for this purpose, frequently reporting accuracy gains. However, such claims are typically based on single training runs, do not match the parameter budgets of the quantum and classical generators, and do not characterize the data regime in which any benefit appears.
arXiv:2606. 18970v1 Announce Type: cross Abstract: Medical image classification is often constrained by limited labeled data, motivating generative augmentation; recently, quantum generative models have been proposed for this purpose, frequently reporting accuracy gains.
arXiv:2608.31117v1 Announce Type: cross Abstract: Generative models have become central across science and industry, from image and text synthesis to the design of molecules and materials. Quantum ge...
arXiv:2401. 07039v5 Announce Type: replace-cross Abstract: Mixed quantum states are the native description of many physically important quantum systems, making their generation a fundamental task in quantum information processing.
The paper introduces TabularQGAN, a quantum generative adversarial network designed to synthesize tabular data with both categorical and numerical features. It proposes flexible data encoding and a novel quantum circuit ansatz, and evaluates the model on MIMIC‑III and Adult Census datasets, benchmarking against classical methods such as CTGAN, CopulaGAN, VAE‑GMM, and an LLM‑based approach. Results from noiseless statevector simulations show competitive or leading performance in overall similarity scores and demonstrate strong generalization through custom metrics.
arXiv:2510. 24598v2 Announce Type: replace Abstract: Current quantum machine learning approaches often face challenges balancing predictive accuracy, robustness, and interpretability.
arXiv:2607. 07072v1 Announce Type: new Abstract: Quantum diffusion models provide a physics-consistent route to generative learning by formulating noising and denoising directly on quantum states.