Quantum machine learning faces challenges with current quantum hardware, especially in generating high-dimensional data such as images. The Quantum Patch GAN [1] offers a solution by generating images in smaller segments or "patches". Each patch is handled by a quantum sub-generator, significantly reducing hardware requirements by minimizing the number of qubits and circuit depth, which are key limitations in today's quantum processors.
Unlike traditional GANs that generate an entire image in one go, the Quantum Patch GAN breaks the image down into smaller, manageable patches. Each patch is independently generated using a parameterized quantum circuit (PQC) that operates on a subset of qubits. These circuits are trained to learn the probability distributions needed to generate each patch, employing techniques such as quantum gradient descent for optimization.
The circuit design for each sub-generator begins with an initial state preparation, followed by a series of entangling gates and single-qubit rotations. These layers capture the complex correlations within the qubits to encode realistic patches of the image. After generating the patches, they are combined classically to form a complete image, which maintains coherence and quality across patches.

Variational Autoencoders (VAEs) [2], another popular model for image generation, could also benefit from a patch-based approach. VAEs encode data into a lower-dimensional latent space and decode it to reconstruct images. Generating an image piece-by-piece would reduce the resources required, which is particularly advantageous in the quantum realm where hardware is currently limited. Adopting a patch-based approach allows both VAEs and Quantum Patch GANs to produce high-quality images more efficiently, making the most of the available quantum hardware.
The Quantum Patch GAN provides an innovative means of tackling the hardware limitations of quantum computers for image generation. By generating images in patches, it optimizes the use of quantum resources, offering a feasible path forward in the NISQ era. This approach could be extended to other generative models such as VAEs, further enhancing the efficiency of quantum machine learning in generating complex, high-dimensional data. As quantum hardware evolves, these strategies could become foundational in developing robust quantum generative models for various applications.
[1] Huang, H. L., Du, Y., Gong, M., Zhao, Y., Wu, Y., Wang, C., ... & Pan, J. W. (2021). Experimental quantum generative adversarial networks for image generation. Physical Review Applied, 16(2), 024051.
[2] Pinheiro Cinelli, L., Araújo Marins, M., Barros da Silva, E. A., & Lima Netto, S. (2021). Variational autoencoder. In Variational Methods for Machine Learning with Applications to Deep Networks (pp. 111-149). Cham: Springer International Publishing.

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