Quantum machine learning offers a promising advantage in extracting information about quantum states, e.g., phase diagram. However, access to training labels is a major bottleneck for any supervised approach, preventing getting insights about new physics. In this talk I will show how using quantum convolutional neural networks, we overcome this limit by determining the phase diagram of a model where analytical solutions are lacking, by training only on marginal points of the phase diagram, where integrable models are represented. More specifically, we consider the axial next-nearest-neighbor Ising Hamiltonian

Despite its simplicity, the ANNNI model reveals a rich and complex phase diagram, making it an ideal testbed for studying the emergence of various phases and their critical behaviors. Our research aims to investigate the model's phase transitions and uncover the distinct features of each phase.