Using quantum computing, Argonne National Laboratory solves the electronic structure of complex materials

If you know the atoms that make up a particular molecule or solid material, then the interactions between these atoms can be determined computationally, at least by solving the quantum mechanical equations (if the molecule is small and simple). While solving these equations is critical for fields ranging from materials engineering to drug design, for complex molecules and materials, it requires very long computational times.

Now, researchers at the U.S. Department of Energy's Argonne National Laboratory, the University of Chicago's Pritzker Institute for Molecular Engineering and the Department of Chemistry have explored the possibility of using quantum computers to solve these electronic structures.

The study, which incorporates new computational methods, is published online in the Journal of Chemical Theory and Computation (JCTC).

"This is an exciting step forward in using quantum computers to solve challenging problems in computational chemistry," said Giulia Galli, who led the research with Argonne scientist Marco Govoni and is a member of the Consortium for Advanced Science and Engineering (CASE) at the University of Chicago.

Predicting the electronic structure of materials involves solving complex equations that determine how electrons interact with each other and modeling how various possible structures compare with each other at the overall energy level.

Unlike traditional computers that store information in binary bits, quantum computers use quantum bits that can exist in a superposition state, making them easier and faster to solve certain problems. Computational chemists have debated whether and when quantum computers will eventually be able to solve the electronic structure of complex materials better than traditional computers. However, today's quantum computers are still relatively small and produce noisy data.

Even with these weaknesses, Galli and her colleagues want to know if they can still make progress in creating the fundamental quantum computing methods needed to solve electronic structure problems on quantum computers.

The question we really wanted to address was how to deal with the current state of quantum computers," said Marco Govoni, a scientist at Argonne National Laboratory and the University of Chicago's Advanced Science and Engineering Alliance. We asked the question: even if the results of quantum computers are noisy, are they still useful for solving interesting problems in materials science?"

The researchers designed a hybrid simulation process using an IBM quantum computer. In their approach, a small number of quantum bits between four and six quantum bits perform part of the computation, and then the results are further processed using a classical computer.

Workflow used to simulate spin-defect ground and excited state energies on a quantum computer

"We designed an iterative computational process that takes advantage of both quantum computers and conventional computers."

After several iterations, the simulation process was able to provide the correct electronic structure of several spin defects in solid-state materials. In addition, the team developed a new error mitigation method to help control the inherent noise generated by quantum computers and to ensure the accuracy of the results.

Spin defects studied in the work

Currently, electronic structures solved using new quantum computing methods can already be solved using conventional computers. Thus, the long-standing debate about whether quantum computers are superior to classical computers in solving electronic structure problems has not been resolved.

However, the results provided by the new method pave the way for quantum computers to handle more complex chemical structures.

In this regard, the research team said, "When we scale it up to 100 quantum bits instead of four or six, we think we may have an advantage over conventional computers. But only time will tell."

The team is planning to continuously improve and expand their method and use it to solve different types of electronic problems, such as molecules in the presence of solvents, as well as molecules and materials in excited states.