A group of scientists at the U.S. Department of Energy’s Ames Laboratory has
developed computational quantum algorithms that are capable of efficient and
highly accurate simulations of static and dynamic properties of quantum
systems. The algorithms are valuable tools to gain greater insight into the
physics and chemistry of complex materials, and they are specifically
designed to work on existing and near-future quantum computers.

Scientist Yong-Xin Yao and his research partners at Ames Lab use the power
of advanced computers to speed discovery in condensed matter physics,
modeling incredibly complex quantum mechanics and how they change over
ultra-fast timescales. Current high performance computers can model the
properties of very simple, small quantum systems, but larger or more complex
systems rapidly expand the number of calculations a computer must perform to
arrive at an accurate model, slowing the pace not only of computation, but
also discovery.

“This is a real challenge given the current early-stage of existing quantum
computing capabilities,” said Yao, “but it is also a very promising
opportunity, since these calculations overwhelm classical computer systems,
or take far too long to provide timely answers.”

The new algorithms tap into the capabilities of existing quantum computer
capabilities by adaptively generating and then tailoring the number and
variety of “educated guesses'' the computer needs to make in order to
accurately describe the lowest-energy state and evolving quantum mechanics
of a system. The algorithms are scalable, making them able to model even
larger systems accurately with existing current “noisy” (fragile and prone
to error) quantum computers, and their near-future iterations.

“Accurately modeling spin and molecular systems is only the first part of
the goal,” said Yao, “In application, we see this being used to solve
complex materials science problems. With the capabilities of these two
algorithms, we can guide experimentalists in their efforts to control
materials’ properties like magnetism, superconductivity, chemical reactions,
and photo-energy conversion.”

“Our long-term goal is to reach ‘quantum advantage’ for materials— to
utilize quantum computing to achieve capabilities that cannot be achieved on
any supercomputer today," said Ames Laboratory Scientist Peter Orth.

This topic is further discussed in two papers: (1)“Adaptive Variational Quantum Dynamics Simulation,” authored by Y.-X. Yao, N. Gomes, F. Zhang, C.-Z. Wang, K.-M. Ho, T.
Iadecola, and P. P. Orth; and published in PRX Quantum; (2) “Adaptive Variational Quantum Imaginary Time Evolution Approach for Ground
State Preparation”, authored by N. Gomes, A. Mukherjee, F. Zhang, T. Iadecola, C.-Z. Wang,
K.-M. Ho, P. P. Orth, Y.-X. Yao; accepted in Advanced Quantum Technologies.

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