Record-breaking quantum simulator could unlock new materials

An unprecedentedly large quantum simulator could shed light on how exotic, potentially useful quantum materials work and help us optimize them in the future.

Quantum computers may eventually use quantum phenomena to complete calculations that are intractable to the world’s best conventional computers. Similarly, a simulator using quantum phenomena could help researchers accurately model poorly understood materials or molecules.

This is especially true for materials such as superconductors, which conduct electricity with near-perfect efficiency because they derive this property from quantum effects that could be directly implemented on quantum simulators but would require multiple steps of mathematical translation on conventional devices.

Michelle Simmons at Silicon Quantum Computing in Australia and her colleagues have now created the largest quantum simulator for quantum materials to date, called Quantum Twins. “The scale and controllability we’ve achieved with these simulators means we’re now ready to tackle some very interesting problems,” he says. “We are designing new materials in a previously unthinkable way by literally building their analogues atom by atom.”

The researchers built several simulators by inserting phosphorus atoms into silicon chips. Each atom became a quantum bit or qubit, the basic building block of quantum computers and simulators, and the team could precisely arrange the qubits into different lattices that emulated the arrangement of atoms in real materials. Each iteration of the quantum twins was made up of a square network of 15,000 qubits – more than any previous quantum simulator. Similar qubit fields have previously been created, for example, from several thousand extremely cold atoms.

Through this patterning process, and by adding electronic components to each chip, the researchers also controlled the properties of the electrons in the chip. This mimicked the control electrons in the simulated materials, which is crucial for understanding the flow of electricity in them, for example. For example, researchers could tune how difficult it would be to add an electron to any point in the lattice, or how difficult it would be for an electron to “jump” between two points.

Simmons says conventional computers struggle to simulate large two-dimensional systems as well as certain combinations of electron properties, but the Quantum Twins simulators have shown promise for these cases. She and her team tested their chips by simulating the transition between metallic (or conductive) and insulating behavior, a famous mathematical model of how “impurity” in a material can affect its ability to support electrical currents. They also measured the system’s “Hall coefficient” as a function of temperature, which captures how the simulated material behaves when exposed to magnetic fields.

The size of the devices used in the experiment and the team’s ability to control variables mean that the Quantum Twins simulators could continue to address unconventional superconductors, Simmons says. How conventional superconductors work at the level of their electrons is fairly well known, but they have to be extremely cold or put under enormous pressure to superconduct, which is impractical. Some superconductors can work in milder conditions, but to engineer them to work at room temperature and pressure, researchers need to understand them more microscopically—the kind of understanding that quantum simulators could offer in the future.

In addition, Quantum Twins could be used to study interfaces between various metals and polyacetylene-like molecules, which could be useful for developing drugs or devices for artificial photosynthesis, Simmons says.

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