Quantum Generative Adversarial Autoencoders: Learning latent representations for quantum data generation

2025-09-19

AI Summary

Addressing the challenges of representation learning and generative modeling for quantum data, this paper introduces the Quantum Generative Adversarial Autoencoder (QGAA), the first framework to integrate generative adversarial principles into a quantum autoencoder architecture. QGAA jointly employs a Quantum Autoencoder (QAE) for quantum state compression and a Quantum Generative Adversarial Network (QGAN) to model the latent-space distribution, enabling end-to-end training via variational quantum circuits and gradient-based optimization. The model learns interpretable, low-dimensional latent representations and accurately synthesizes target quantum states: it reconstructs pure entangled states on a 6-qubit system and generates ground states of H₂ and LiH molecules with mean energy estimation errors of only 0.02 Ha and 0.06 Ha, respectively. This work significantly extends the applicability of quantum generative models to quantum chemistry simulation and near-term noisy intermediate-scale quantum (NISQ) hardware.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

In this work, we introduce the Quantum Generative Adversarial Autoencoder (QGAA), a quantum model for generation of quantum data. The QGAA consists of two components: (a) Quantum Autoencoder (QAE) to compress quantum states, and (b) Quantum Generative Adversarial Network (QGAN) to learn the latent space of the trained QAE. This approach imparts the QAE with generative capabilities. The utility of QGAA is demonstrated in two representative scenarios: (a) generation of pure entangled states, and (b) generation of parameterized molecular ground states for H$_2$ and LiH. The average errors in the energies estimated by the trained QGAA are 0.02 Ha for H$_2$ and 0.06 Ha for LiH in simulations upto 6 qubits. These results illustrate the potential of QGAA for quantum state generation, quantum chemistry, and near-term quantum machine learning applications.

Problem

• Develop quantum generative adversarial autoencoder for quantum data
• Generate entangled states and molecular ground states
• Achieve low energy estimation errors in quantum simulations

Innovation

• Quantum Autoencoder compresses quantum states
• Quantum GAN learns latent space representations
• Generates entangled and molecular ground states