A normalising-flow-assisted neural quantum state method estimates ground state energies of Ising chains with up to 50 spins, matching matrix product states for long-range interactions.
Towards a Quantum Simulation of Nonlinear Sigma Models with a Topological Term
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abstract
We determine the mass gap of a two-dimensional $O(3)$ nonlinear sigma model augmented with a topological $\theta$-term using tensor network and digital quantum algorithms. As proof of principle, we consider the example $\theta = \pi$ and study its critical behaviour on a quantum simulator by examining the entanglement entropy of the ground state. We confirm that the quantum theory is massless in the strong-coupling regime, in agreement with analytical results. However, we also highlight the limitations of current quantum algorithms, designed for noisy intermediate-scale quantum devices, in the theory simulation at weak coupling. Finally, we compare the performance of our quantum algorithms to classical tensor network methods.
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Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States
A normalising-flow-assisted neural quantum state method estimates ground state energies of Ising chains with up to 50 spins, matching matrix product states for long-range interactions.