Quantum Generative Adversarial Autoencoders: Learning latent representations for quantum data generation
This addresses the challenge of quantum data generation for applications in quantum chemistry and near-term quantum machine learning, representing an incremental advancement by integrating existing quantum components.
The paper tackles the problem of generating quantum data by introducing the Quantum Generative Adversarial Autoencoder (QGAA), which combines a quantum autoencoder and a quantum generative adversarial network to learn latent representations, achieving average energy errors of 0.02 Ha for H2 and 0.06 Ha for LiH in simulations.
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.