Simulations using a quantum computer show the current limits of the technology

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Quantum circuits still cannot outperform classical ones when simulating molecules.

E. Lucero/Google

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

Quantum computers promise to directly simulate systems governed by quantum principles, such as molecules or materials, since quantum bits themselves are quantum objects. Recent experiments have shown the power of these devices in performing carefully selected tasks. But a new study shows that for real-world problems of interest, such as calculating the energy states of a group 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 that they could 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 today’s classical computers [2]. But this “quantum advantage” is achieved only for selected computational tasks that take advantage of the strengths of these devices. How well do these quantum computers fare for the kinds of everyday challenges that researchers studying molecules and materials really want to solve?

Garnet Chan of the California Institute of Technology and her coworkers set out to answer this question by running one-molecule and one-material simulations using a 53-qubit Google processor called Weber, based on Sycamore. “We don’t 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 Sycamore hardware performs for a physically relevant class of circuits with a physically relevant success metric.”

The team selected two problems of current interest, without considering how well suited they might be to a quantum circuit. The first consists of calculating the energy states of a group of 8 iron (Fe) and sulfur (S) atoms found in the catalytic nucleus of the nitrogenase enzyme. This enzyme breaks strong bonds in nitrogen molecules as the first step in an important biological process called nitrogen fixation. Understanding the chemistry of this process could be valuable in developing artificial nitrogen-fixing catalysts for the chemical industry.

catalytic core. At the catalytic site of the nitrogenase enzyme, responsible for the removal of nitrogen from the atmosphere (nitrogen fixation), is a cluster of iron (red) and sulfur (yellow) atoms that catalyzes the splitting of nitrogen molecules. The researchers would like to simulate this process on a quantum computer to develop artificial nitrogen-fixation catalysts.catalytic core. At the catalytic site of the nitrogenase enzyme, responsible for the removal of nitrogen from the atmosphere (nitrogen fixation), is a cluster of iron (red) and sulfur (yellow) atoms that catalyzes the splitting of nitrogen molecules. .. Show more

Second, the team sought to deduce the collective behavior of magnetic spins in crystalline alpha-ruthenium trichloride (

𝛼

-RuCl3), which is believed to adopt an exotic quantum phase called a spin liquid at low temperatures [3]. Studying such states is part of the larger project of exploring quantum phenomena in materials.

The basic electronic states and low-energy excitations of the two systems are determined by how the electronic spins of the atoms interact with each other. These spins could be encoded in individual qubits and their interactions simulated by coupling the qubits into circuits that mirror the structures of the two systems.

One of the key obstacles to accurate quantum simulations is noise: random errors both in switching the “gates” that perform quantum logic operations and in reading their output states. These errors accumulate and restrict the number of gate operations that a computation can perform before noise dominates. The researchers found that simulations with more than 300 doors were overwhelmed by noise. But the more complex the system, the more doors are needed. The Fe-S group, for example, has long-range interactions between spins; to be accurately represented, such interactions require many gates.

Due to these challenges, on-chip Weber simulations were quite limited. For example, the simulations provided predictions for the energy spectra of the Fe-S cluster and the heat capacity of

𝛼

-RuCl3 reasonably well, but only if the simulated systems were not too large. For

𝛼

-RuCl3 the team was only able to obtain significant results for a very small 6-atom fragment of the crystal lattice; if they increased the size to just 10 atoms, the noise overwhelmed the output. And the constraints on gate operations meant that only a fifth of Weber’s quantum resources could be used for computation. However, Chan and his colleagues were able to increase this usage to half the resources when they switched to simulating a model system more suited to Weber’s specific circuit architecture.

Chan says it’s hard to see quantum circuitry working much better for problems like this until there are better ways to reduce noise or fix bugs. (The schemes developed so far do not allow full correction of the quantum error.)

“These results are state-of-the-art and show the challenges to be overcome in terms of future device performance,” says Alán Aspuru-Guzik of the University of Toronto, a specialist in the use of quantum computing in chemistry and materials. But the capabilities have increased steadily since the first quantum computers in the 2000s, as demonstrated by this new work, he says. Peter Love, a specialist in quantum simulations at Tufts University in Massachusetts, is optimistic about the findings. “These results are both exciting and discouraging,” he says. “Compared to our expectations in 2005, they are absolutely amazing, but they also show how much work lies ahead of us.”

–Felipe Bola

Philip Ball is a freelance science writer in London. His last book is modern myths (University of Chicago Press, 2021).

References

  1. RN Tazhigulov et al.“Simulating challenging correlated material and molecule models on the Sycamore quantum processor”. Quantum PRX 3040318 (2022).
  2. F. Arute et al.“Quantum Supremacy Using a Programmable Superconducting Processor,” Nature 574505 (2019).
  3. hli et al.“Giant phonon anomalies in the near-quantum spin Kitaev liquid
    𝛼

    RuCl3

    ,” common nat. 123513 (2021).


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