• Physics 15, 175
Quantum circuits still cannot outperform classical circuits in simulating molecules.
Quantum computers promise to directly simulate systems governed by quantum principles, such as molecules or materials, because quantum bits themselves are quantum objects. Recent experiments have demonstrated the power of these devices in performing carefully selected tasks. But a new study shows that for real-world problems, such as calculating the energy states of a cluster of atoms, quantum simulations are no more accurate than classical computers. . 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, saying that they could be used to calculate the properties of quantum matter. Quantum processors with several hundred quantum bits (qubits) are available today, and some can in principle represent quantum states that cannot be encoded in any classical device. The 53-qubit Sycamore processor developed by Google has demonstrated the potential to perform calculations in days that would take many millennia on today’s classical computers. . However, this “quantum advantage” is only achieved for selected computational tasks that play to the strengths of these devices. How well do such quantum computers do with the kinds of everyday problems that researchers studying molecules and materials actually want to solve?
Garnet Chan of the California Institute of Technology and his collaborators set out to answer this question by performing simulations of the molecule and material using a 53-qubit Google processor called Weber, based on Sycamore. “We didn’t expect to learn anything new chemically, given how complex these systems are and how good the 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 success metric.”
The team chose two problems of current interest without considering how well they might be suited to a quantum circuit. The first involves the calculation of the energy states of the 8-atom cluster of iron (Fe) and sulfur (S) found in the catalytic core of the nitrogenase enzyme. This enzyme breaks the 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 for the development of artificial nitrogen-fixing catalysts for the chemical industry.
Second, the team sought to derive the collective behavior of magnetic spins in the crystalline material alpha-ruthenium trichloride (
-RuCl3), which is believed to host an exotic quantum phase called a spin liquid at low temperatures . The study of such states is part of a larger project investigating quantum phenomena in materials.
The electronic ground states and low-energy excitations of these two systems are determined by how the electron spins of the atoms interact with each other. These spins could be encoded into individual qubits and their interactions simulated by connecting the qubits in circuits that mirror the structures of the two systems.
One of the key obstacles to accurate quantum simulations is noise—random errors in both the switching of the “castles” that perform quantum logic operations and the reading of their output states. These errors accumulate and limit the number of gate operations the calculation can perform before the noise dominates. The researchers found that simulations with more than 300 gates were overwhelmed by noise. But the more complex the system, the more gates are needed. For example, the Fe-S cluster has long-range interactions between spins; to be accurately represented, such interactions require many gates.
Because of these challenges, simulations on the Weber chip were rather limited. For example, the simulations provided predictions for the Fe-S cluster energy spectra and heat capacity
-RuCl3 fairly well – but only if the simulated systems were not too large. For
-RuCl3 the team could only get meaningful results for a very small 6-atom piece of the crystal lattice; if they increased the size to just 10 atoms, the noise drowned out the output. And the limitations of gate operations meant that only about one-fifth of Weber’s quantum resources could be used for the calculation. However, Chan and colleagues were able to increase this utilization to half the resources when they switched to simulating a model system that better suited the specific architecture of Weber circuits.
Chan says it’s hard to see quantum circuits doing much better for problems like this until there are better ways to reduce noise or correct errors. (Schemes developed so far do not allow for full quantum error correction.)
“These results are state-of-the-art and show the challenges that need 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 capabilities have steadily increased since the first quantum computers in 2000, as this new work shows, he says. Peter Love, a quantum simulation specialist at Tufts University in Massachusetts, is optimistic about the findings. “These results are both exciting and daunting,” he says. “Compared to our expectations in 2005, they are absolutely amazing, but also show how much work we still have to do.”
– Philip Ball
Philip Ball is a freelance science writer in London. His latest book is Modern myths (University of Chicago Press, 2021).
- RN Tazhigulov et al.“Simulation of Challenging Correlated Molecules and Materials Models on the Sycamore Quantum Processor,” PRX Quantum 3040318 (2022).
- F. Arrival et al.“Quantum Supremacy Using a Programmable Superconducting Processor,” Nature 574505 (2019).
- H. Li et al.“Giant phonon anomalies in a near Kitaev quantum spin liquid
” Nat. Common. 123513 (2021).
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