Revisiting Majumdar-Ghosh spin chain model and Max-cut problem using variational quantum algorithms

TL;DR

This paper applies variational quantum algorithms to analyze the Majumdar-Ghosh spin chain model and Max-cut problem, exploring their effectiveness in noisy quantum simulations and comparing classical optimizers.

Contribution

It demonstrates the use of QAOA and VQE for complex spin models and Max-cut, and compares classical optimizers to improve algorithm performance in noisy quantum environments.

Findings

QNSPSA optimizer enhances QAOA convergence.

VQE with EfficientSU2 and SPSA yields best results.

Successful application of variational algorithms to non-exactly solvable models.

Abstract

In this work, energy levels of the Majumdar-Ghosh model (MGM) are analyzed up to 15 spins chain in the noisy intermediate-scale quantum framework using noisy simulations. This is a useful model whose exact solution is known for a particular choice of interaction coefficients. We have solved this model for interaction coefficients other than that required for the exactly solvable conditions as this solution can be of help in understanding the quantum phase transitions in complex spin chain models. The solutions are obtained using quantum approximate optimization algorithms (QAOA), and variational quantum eigensolver (VQE). To obtain the solutions, the one-dimensional lattice network is mapped to a Hamiltonian that corresponds to the required interaction coefficients among spins. Then, the ground states energy eigenvalue of this Hamiltonian is found using QAOA and VQE. Further, the validity of the Lieb-Schultz-Mattis theorem in the context of MGM is established by employing variational quantum deflation to find the first excited energy of MGM. Solution for an unweighted Max-cut graph for 17 nodes is also obtained using QAOA and VQE to know which one of these two techniques performs better in a combinatorial optimization problem. Since the variational quantum algorithms used here to revisit the Max-cut problem and MGM are hybrid algorithms, they require classical optimization. Consequently, the results obtained using different types of classical optimizers are compared to reveal that the QNSPSA optimizer improves the convergence of QAOA in comparison to the SPSA optimizer. However, VQE with EfficientSU2 ansatz using the SPSA optimizer yields the best results.