Year: 2024
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Cea, M., Grossi, M., Monaco, S., Rico, E., Tagliacozzo, L., Vallecorsa, S.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1622782223, G1768993395, FOR 5522: Quantenthermalisierung, Lokalisierung und eingeschränkte Dynamik mit wechselwirkenden ultrakalten Atomen, G2262748287, G2744928646, G3480869486, G3681454997, G3777708657, G3861891927, G4042783231, G425668148, G4454383352, G4511186732, G451917667, G4710139657, G524312865, G5629799415, G586857820, G5967599077, G6293336981, G651457789, G67347242, G7084143925, G8138842782, EXC 2111: Munich Center for Quantum Science and Technology (MCQST), G8953647516
OpenAlex ID: W4392011891