Simulations Using a Quantum Computer Show the Technology’s Current Limits

• Physics 15, 175

Quantum circuits still can’t outperform classical ones when simulating molecules.

E. Lucero/Google

Get real. The Sycamore chip made by Google, comprising 53 quantum bits, has been used to explore “quantum advantage”: a performance in quantum computation that exceeds anything possible on classical computers. But how well does this type of device perform in common problems of real-world interest, such as quantum simulations of molecules and materials?Get real. The Sycamore chip made by Google, comprising 53 quantum bits, has been used to explore “quantum advantage”: a performance in quantum computation that exceeds anything possible on classical computers. But how well does this type of device pe… Show more

Quantum computers promise to directly simulate systems governed by quantum principles, such as molecules or materials, since the quantum bits themselves are quantum objects. Recent experiments have demonstrated the power of these devices when performing carefully chosen tasks. But a new study shows that for problems of real-world interest, such as calculating the energy states of a cluster of atoms, quantum simulations are no more accurate than those of classical computers [1]. The results offer a benchmark for judging how close quantum computers are to becoming useful tools for chemists and materials scientists.

Richard Feynman proposed the idea of quantum computers in 1982, suggesting they might be used to calculate the properties of quantum matter. Today, quantum processors are available with several hundred quantum bits (qubits), and some can, in principle, represent quantum states that are impossible to encode in any classical device. The 53-qubit Sycamore processor developed by Google has demonstrated the potential to perform calculations in a few days that would take many millennia on current classical computers [2]. But this “quantum advantage” is achieved only for selected computational tasks that play to these devices’ strengths. How well do such quantum computers fare for the sorts of everyday challenges that researchers studying molecules and materials actually wish to solve?

Garnet Chan of the California Institute of Technology and his co-workers set out to answer this question by performing simulations of a molecule and a material using a 53-qubit Google processor called Weber, based on Sycamore. “We did not anticipate learning anything new chemically, given how complex these systems are and how good classical algorithms are,” says Chan. “The goal was to understand how well the Sycamore hardware performs for a physically relevant class of circuits with a physically relevant metric of success.”

The team selected two problems of current interest, without any consideration of how well suited they might be to a quantum circuit. The first involves calculating the energy states of

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Climate Scientists Encounter Limits of Computer Models, Bedeviling Policy

BOULDER, Colo.—For almost five years, an international consortium of scientists was chasing clouds, determined to solve a problem that bedeviled climate-change forecasts for a generation: How do these wisps of water vapor affect global warming?

They reworked 2.1 million lines of supercomputer code used to explore the future of climate change, adding more-intricate equations for clouds and hundreds of other improvements. They tested the equations, debugged them and tested again.

The scientists would find that even the best tools at hand can’t model climates with the sureness the world needs as rising temperatures impact almost every region.

When they ran the updated simulation in 2018, the conclusion jolted them: Earth’s atmosphere was much more sensitive to greenhouse gases than decades of previous models had predicted, and future temperatures could be much higher than feared—perhaps even beyond hope of practical remedy.

“We thought this was really strange,” said Gokhan Danabasoglu, chief scientist for the climate-model project at the Mesa Laboratory in Boulder at the National Center for Atmospheric Research, or NCAR. “If that number was correct, that was really bad news.”

At least 20 older, simpler global-climate models disagreed with the new one at NCAR, an open-source model called the Community Earth System Model 2, or CESM2, funded mainly by the U.S. National Science Foundation and arguably the world’s most influential climate program. Then, one by one, a dozen climate-modeling groups around the world produced similar forecasts. “It was not just us,” Dr. Danabasoglu said.

‘You solve one problem and create another,’ says Andrew Gettelman, right, at the NCAR Mesa Laboratory; left, NCAR’s Gokhan Danabasoglu.

The scientists soon concluded their new calculations had been thrown off kilter by the physics of clouds in a warming world, which may amplify or damp climate change. “The old way is just wrong, we know that,” said Andrew Gettelman, a physicist at NCAR who specializes in clouds and helped develop the CESM2 model. “I think our higher sensitivity is wrong too. It’s probably a consequence of other things we did by making clouds better and more realistic. You solve one problem and create another.”

Since then the CESM2 scientists have been reworking their climate-change algorithms using a deluge of new information about the effects of rising temperatures to better understand the physics at work. They have abandoned their most extreme calculations of climate sensitivity, but their more recent projections of future global warming are still dire—and still in flux.

As world leaders consider how to limit greenhouse gases, they depend heavily on what computer climate models predict. But as algorithms and the computer they run on become more powerful—able to crunch far more data and do better simulations—that very complexity has left climate scientists grappling with mismatches among competing computer models.

While vital to calculating ways to survive a warming world, climate models are hitting a wall. They are running up against the complexity of the physics involved; the limits of scientific computing; uncertainties around the nuances of climate behavior; and the challenge

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