An adaptive method for modelling materials was used by a group of scientists from the Ames National Laboratory of the U.S. Department of Energy to demonstrate how to progress the application of quantum computing in materials research. The use of an adaptive algorithm enables quantum computers, which have potential capabilities much beyond those of current computers, to generate accurate and timely results.
Comparing quantum computers to current computers is like comparing apples to oranges. They are composed of quantum bits, or qubits, which have substantially greater information storage capacity than the bits used in modern computers. Quantum computers are able to perform calculations that are not yet achievable with classical computers because of their special powers.
Researchers at Ames Lab are attempting to use quantum computing to their advantage in order to speed up and simplify the study of materials. Rare earth elements are a main area of research at Ames Lab. Smart phones, computer hard drives, light-emitting diodes (LEDs), electronic displays, and permanent magnets for alternative energy technology, such wind turbines, all make use of these materials.
Because rare earth materials are expensive and have a small geographic distribution, relying on them is difficult. Researchers at Ames Lab are looking for substitutes for rare earths that are less expensive and more readily available. Researchers must have a deeper comprehension of rare earths and how they behave in diverse materials and applications in order to complete this goal. The use of quantum computers in this research has the potential to increase productivity and accelerate scientific progress.
Because of their intricate electronic structures, rare earth elements are currently difficult to replicate effectively on a computer, according to Yongxin Yao, a scientist at Ames Lab.
His team’s method is founded on impurity models, which explain magnetic impurities in materials. These models also account for the interaction of the impurity with the rest of the material, which aids in capturing the electrical properties. To model the bulk materials, they also use quantum embedding techniques.
Quantum embedding here refers to a two-dimensional rendering of the substance. To enable these simulations, the researchers applied a methodical approach to simplification of the bulk material representation. While retaining accuracy, quantum embedding uses less computational power.
“In order to reduce the error in our calculations, we need compact quantum circuits,” Yao explained. “A variety of paths, specifically quantum circuits composed of a set of hardware operations, can move the system from an initial point to the final point you want to reach. Because of the error associated with each operation, you want the shortest path.”
The technique employed by Yao’s team is intended to automatically find the shortest routes to the desired state. According to him, this effort is a crucial first step in the direction of being able to model entire systems of genuine materials. When this technology is fully developed, it will be able to more effectively aid material scientists in the discovery and design of new materials for particular applications.
“We have developed some adaptive way to construct compact quantum circuits for either static or dynamic simulations. This work is a first comprehensive application of the adaptive method for impurity models derived from real materials,” said Yao. “So that’s an important step toward real materials simulations on quantum computers.”
This research is further discussed in the paper, “Comparative study of adaptive variational quantum eigensolvers for multi-orbital impurity models.” The paper is published in the journal Communications Physics.
