Exploring the Phase Diagram of the quantum one-dimensional ANNNI model
This work presents a circuit-level integration of Quantum Machine Learning and Tensor Networks in the context of the one-dimensional Axial Next-Nearest-Neighbor Ising model with a transverse field. The study concretely connects these two paradigms: ground states of the model are obtained via the density matrix renormalization group as Matrix Product States and converted into quantum circuits, which are then used as input to a hybrid quantum-classical classifier and a Quantum Autoencoder by prepending the state-preparation circuit to the trainable ansatz. This construction directly addresses a key bottleneck of purely quantum workflows, namely the need for expensive, gradient-based optimization on hardware for every input state, by delegating state preparation to classical Tensor Network methods, which are scalable by construction. The result is a scalable and hardware-compatible pipeline for the task of studying spin systems.