aiqbench analysis
This research proposes a hybrid workflow that leverages classical Tensor Networks to prepare quantum states, effectively bypassing the costly gradient-based optimization typically required on quantum hardware for studying spin systems. By integrating Matrix Product States with Quantum Autoencoders, the study creates a scalable pipeline specifically designed for analyzing complex one-dimensional quantum models like the ANNNI model. The approach directly addresses efficiency bottlenecks in purely quantum workflows by offloading state preparation to classical methods without sacrificing hardware compatibility.
Exploring the Phase Diagram of the quantum one-dimensional ANNNI model
arXiv:2402.11022v2 Announce Type: replace-cross 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.
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.
Strongly Correlated Electrons (cond-mat.str-el); Disordered Systems and Neural Networks (cond-mat.dis-nn); Statistical Mechanics (cond-mat.stat-mech); Quantum Physics (quant-ph)
arXiv:2402.11022 [cond-mat.str-el]
(or arXiv:2402.11022v2 [cond-mat.str-el] for this version)
Full text under the publisher's open licence · 157 words
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