Quantum Generative

Quantum generative models aim to leverage the principles of quantum mechanics to create powerful generative models capable of producing realistic and diverse data samples. Current research focuses on developing and benchmarking various architectures, including quantum versions of adversarial autoencoders, diffusion models, and Born machines, often hybridized with classical methods to address limitations of current quantum hardware. These models show promise in diverse applications, such as drug discovery, materials science, financial modeling, and neuroscience, primarily by offering improved efficiency and potentially enhanced performance compared to classical approaches, particularly in handling high-dimensional or complex data. The field is actively developing standardized benchmarking techniques to rigorously assess the capabilities and limitations of these emerging quantum methods.