# Learning and Inference on Generative Adversarial Quantum Circuits

arXiv: https://arxiv.org/abs/1808.03425 · HTML: https://mlsanity.com/paper/1808.03425/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2018.

## Built on by (most cited first, 22 of 22)

- [Recent Advances for Quantum Neural Networks in Generative Learning](https://mlsanity.com/paper/2206.03066/) (2022)
- [Style-based quantum generative adversarial networks for Monte Carlo events](https://mlsanity.com/paper/2110.06933/) (2021)
- [Variational inference with a quantum computer](https://mlsanity.com/paper/2103.06720/) (2021)
- [Learning quantum data with the quantum Earth Mover's distance](https://mlsanity.com/paper/2101.03037/) (2021)
- [A Primer on Quantum Machine Learning](https://mlsanity.com/paper/2511.15969/) (2025)
- [Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time-Series](https://mlsanity.com/paper/2108.03190/) (2021)
- [Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble](https://mlsanity.com/paper/2401.07039/) (2024)
- [Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage](https://mlsanity.com/paper/2507.19861/) (2025)
- [Quantum Optimization for Training Quantum Neural Networks](https://mlsanity.com/paper/2103.17047/) (2021)
- [Power of Quantum Generative Learning](https://mlsanity.com/paper/2205.04730/) (2022)
- [F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits](https://mlsanity.com/paper/2110.04253/) (2021)
- [A hybrid quantum-classical approach for inference on restricted Boltzmann machines](https://mlsanity.com/paper/2304.12418/) (2023)
- [Variational Quantum Circuits Enhanced Generative Adversarial Network](https://mlsanity.com/paper/2402.01791/) (2024)
- [Quantum Deep Learning for Mutant COVID-19 Strain Prediction](https://mlsanity.com/paper/2203.03556/) (2022)
- [Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models](https://mlsanity.com/paper/2408.00673/) (2024)
- [A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor](https://mlsanity.com/paper/2412.16867/) (2024)
- [QGAN-based data augmentation for hybrid quantum-classical neural networks](https://mlsanity.com/paper/2505.24780/) (2025)
- [ReCon: Reconfiguring Analog Rydberg Atom Quantum Computers for Quantum Generative Adversarial Networks](https://mlsanity.com/paper/2408.13389/) (2024)
- [Sample-efficient Quantum Born Machine through Coding Rate Reduction](https://mlsanity.com/paper/2211.10418/) (2022)
- [Quantum Wasserstein GANs for State Preparation at Unseen Points of a Phase Diagram](https://mlsanity.com/paper/2309.09543/) (2023)
- [Distribution alignment based transfer fusion frameworks on quantum devices for seeking quantum advantages](https://mlsanity.com/paper/2411.01822/) (2024)
- [Do Quantum AIs Dream in Paths? Path-Integral Slow Thinking through Grover Interference](https://mlsanity.com/paper/2609.05842/) (2026)

## Abstract (arXiv)

Quantum mechanics is inherently probabilistic in light of Born's rule. Using quantum circuits as probabilistic generative models for classical data exploits their superior expressibility and efficient direct sampling ability. However, training of quantum circuits can be more challenging compared to classical neural networks due to lack of efficient differentiable learning algorithm. We devise an adversarial quantum-classical hybrid training scheme via coupling a quantum circuit generator and a classical neural network discriminator together. After training, the quantum circuit generative model can infer missing data with quadratic speed up via amplitude amplification. We numerically simulate the learning and inference of generative adversarial quantum circuit using the prototypical Bars-and-Stripes dataset. Generative adversarial quantum circuits is a fresh approach to machine learning which may enjoy the practically useful quantum advantage on near-term quantum devices.