Quantum annealing approach to Ionic Diffusion in Solid

TL;DR

This paper introduces a quantum annealing framework for calculating ionic diffusion correlation factors in solids, linking microstructural data to macroscopic diffusion properties, and compares its performance with traditional methods.

Contribution

It maps ionic diffusion problems into quantum spin systems and demonstrates the potential of quantum annealing to outperform classical approaches in the future.

Findings

Quantum annealing yields consistent results with classical methods.

Conventional methods are computationally infeasible for complex models.

Quantum annealing has future potential as technology advances.

Abstract

We have developed a framework for using quantum annealing computation to evaluate a key quantity in ionic diffusion in solids, the correlation factor. Existing methods can only calculate the correlation factor analytically in the case of physically unrealistic models, making it difficult to relate microstructural information about diffusion path networks obtainable by current ${ab\ initio}$ techniques to macroscopic quantities such as diffusion coefficients. We have mapped the problem into a quantum spin system described by the Ising Hamiltonian. By applying our framework in combination with ab initio technique, it is possible to understand how diffusion coefficients are controlled by temperatures, pressures, atomic substitutions, and other factors.We have calculated the correlation factor in a simple case with a known exact result by a variety of computational methods, including simulated quantum annealing on the spin models, the classical random walk, the matrix description, and quantum annealing on D-Wave with hybrid solver. This comparison shows that all the evaluations give consistent results with each other, but that many of the conventional approaches require infeasible computational costs. Quantum annealing is also currently infeasible because of the cost and scarcity of Q-bits, but we argue that when technological advances alter this situation, quantum annealing will easily outperform all existing methods.