# Adversarial quantum circuit learning for pure state approximation

arXiv: https://arxiv.org/abs/1806.00463 · HTML: https://mlsanity.com/paper/1806.00463/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2018.

## Built on by (most cited first, 16 of 16)

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- [Power of Quantum Generative Learning](https://mlsanity.com/paper/2205.04730/) (2022)
- [Variational Quantum Circuits Enhanced Generative Adversarial Network](https://mlsanity.com/paper/2402.01791/) (2024)
- [QGAN-based data augmentation for hybrid quantum-classical neural networks](https://mlsanity.com/paper/2505.24780/) (2025)
- [Energy-dependent barren plateau in bosonic variational quantum circuits](https://mlsanity.com/paper/2305.01799/) (2023)
- [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)
- [Differentiable Energy-Based Regularization in GANs: A Simulator-Based Exploration of VQE-Inspired Auxiliary Losses](https://mlsanity.com/paper/2512.12581/) (2025)
- [Towards quantum machine learning for assessing the resilience of post-quantum cryptography](https://mlsanity.com/paper/2607.13722/) (2026)

## Abstract (arXiv)

Adversarial learning is one of the most successful approaches to modelling high-dimensional probability distributions from data. The quantum computing community has recently begun to generalize this idea and to look for potential applications. In this work, we derive an adversarial algorithm for the problem of approximating an unknown quantum pure state. Although this could be done on universal quantum computers, the adversarial formulation enables us to execute the algorithm on near-term quantum computers. Two parametrized circuits are optimized in tandem: One tries to approximate the target state, the other tries to distinguish between target and approximated state. Supported by numerical simulations, we show that resilient backpropagation algorithms perform remarkably well in optimizing the two circuits. We use the bipartite entanglement entropy to design an efficient heuristic for the stopping criterion. Our approach may find application in quantum state tomography.