Quantum Generative Adversarial Autoencoders: Learning latent representations for quantum data generation
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912594874335232 |
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| author | Raj, Naipunnya Sangle, Rajiv Singh, Avinash Sabapathy, Krishna Kumar |
| author_facet | Raj, Naipunnya Sangle, Rajiv Singh, Avinash Sabapathy, Krishna Kumar |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_16186 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Generative Adversarial Autoencoders: Learning latent representations for quantum data generation Raj, Naipunnya Sangle, Rajiv Singh, Avinash Sabapathy, Krishna Kumar Quantum Physics Machine Learning 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. |
| title | Quantum Generative Adversarial Autoencoders: Learning latent representations for quantum data generation |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2509.16186 |