Problem-informed Graphical Quantum Generative Learning

Understand

Leveraging the intrinsic probabilistic nature of quantum systems, generative quantum machine learning (QML) offers the potential to outperform classical learning models.

• Current generative QML algorithms mostly rely on general-purpose models that, while being very expressive, face several training challenges.
• One potential way to address these setbacks is by constructing problem-informed models that are capable of more efficient training on structured problems.
• In particular, probabilistic graphical models provide a flexible framework for representing structure in generative learning problems and can thus be exploited to incorporate inductive bias into QML algorithms.