A multi-institutional team became the first to generate accurate results from materials science simulations on a quantum computer that can be verified with neutron scattering experiments and other practical techniques.

Researchers from the Department of Energy’s Oak Ridge National Laboratory; the University of Tennessee, Knoxville; Purdue University and D-Wave Systems harnessed the power of quantum annealing, a form of quantum computing, by embedding an existing model into a quantum computer.

Characterizing materials has long been a hallmark of classical supercomputers, which encode information using a binary system of bits that are each assigned a value of either 0 or 1. But quantum computers — in this case, D-Wave’s 2000Q — rely on qubits, which can be valued at 0, 1 or both simultaneously because of a quantum mechanical capability known as superposition.

«The underlying method behind solving materials science problems on quantum computers had already been developed, but it was all theoretical,» said Paul Kairys, a student at UT Knoxville’s Bredesen Center for Interdisciplinary Research and Graduate Education who led ORNL’s contributions to the project. «We developed new solutions to enable materials simulations on real-world quantum devices.»

This unique approach proved that quantum resources are capable of studying the magnetic structure and properties of these materials, which could lead to a better understanding of spin liquids, spin ices and other novel phases of matter useful for data storage and spintronics applications. The researchers published the results of their simulations — which matched theoretical predictions and strongly resembled experimental data — in PRX Quantum.

Eventually, the power and robustness of quantum computers could enable these systems to outperform their classical counterparts in terms of both accuracy and complexity, providing precise answers to materials science questions instead of approximations. However, quantum hardware limitations previously made such studies difficult or impossible to complete.

Story Source: Materials provided by DOE/Oak Ridge National Laboratory. Original written by Elizabeth Rosenthal. Note: Content may be edited for style and length.