SSDMs introduce an intrinsic score-based diffusion framework on the Fubini-Study manifold to sample quantum pure-state ensembles without classical re-preparation.
Quantum generative adversarial networks
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
abstract
Quantum machine learning is expected to be one of the first potential general-purpose applications of near-term quantum devices. A major recent breakthrough in classical machine learning is the notion of generative adversarial training, where the gradients of a discriminator model are used to train a separate generative model. In this work and a companion paper, we extend adversarial training to the quantum domain and show how to construct generative adversarial networks using quantum circuits. Furthermore, we also show how to compute gradients -- a key element in generative adversarial network training -- using another quantum circuit. We give an example of a simple practical circuit ansatz to parametrize quantum machine learning models and perform a simple numerical experiment to demonstrate that quantum generative adversarial networks can be trained successfully.
citation-role summary
background 4
citation-polarity summary
verdicts
UNVERDICTED 6roles
background 4polarities
background 4representative citing papers
QuanForge introduces statistical mutation killing and nine post-training mutation operators for QNNs to distinguish test suites and localize vulnerable circuit regions.
Q-SYNTH is a hybrid framework using a parameterized quantum circuit as the generator in a GAN to create synthetic minority-class fraud samples for tabular data, which shows reduced distribution mismatch compared to classical GANs and competitive performance in downstream detection tasks.
Brickwall circuits from Haar-random, dual-unitary, and solvable two-qubit gates are tested as quantum reservoirs, showing effective fading memory and prediction accuracy on synthetic time-series data.
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.
A literature review of VQAs covering ansatz design, classical optimization, barren plateaus, error mitigation strategies, and theoretical adaptations for fault-tolerant quantum computing.
citing papers explorer
-
A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics
A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.