While quantum computers continue to slowly grind towards usefulness, some are pursuing a different approach—analog quantum simulation. This path doesn’t offer complete control of single bits of quantum information, known as qubits—it is not a universal quantum computer. Instead, quantum simulators directly mimic complex, difficult-to-access things, like individual molecules, chemical reactions, or novel materials. What analog quantum simulation lacks in flexibility, it makes up for in feasibility: quantum simulators are ready now.
“Instead of using qubits, as you would typically in a quantum computer, we just directly encode the problem into the geometry and structure of the array itself,” says Sam Gorman, quantum systems engineering lead at Sydney-based start-up Silicon Quantum Computing.
Yesterday, Silicon Quantum Computing unveiled its Quantum Twins product, a silicon quantum simulator, which is now available to customers through direct contract. Simultaneously, the team demonstrated that their device, made up of fifteen thousand quantum dots, can simulate an often-studied transition of a material from an insulator to a metal, and all the states between. They published their work this week in the journal Nature.
“We can do things now that we think nobody else in the world can do,” Gorman says.
The powerful process
Though the product announcement came yesterday, the team at Silicon Quantum Computing established its Precision Atom Qubit Manufacturing process following the startup’s establishment in 2017, building on the academic work that the company’s founder, Michelle Simmons, led for over 25 years. The underlying technology is a manufacturing process for placing single phosphorus atoms in silicon with sub-nanometer precision.
“We have a 38-stage process,” Simmons says, for patterning phosphorus atoms into silicon. The process starts with a silicon substrate, which gets coated with a layer of hydrogen. Then, using a scanning-tunneling microscope, individual hydrogen atoms are knocked off the surface, exposing the silicon underneath. The surface is then dosed with phosphine gas, which adsorbs to the surface only in places where the silicon is exposed. With the help of a low temperature thermal anneal, the phosphorus atom is then incorporated into the silicon crystal. Then, layers of silicon are grown on top.
“It’s done in ultra-high vacuum. So it’s a very pure, very clean system,” Simmons says. “It’s a fully monolithic chip that we make with that sub-nanometer precision. In 2014, we figured out how to make markers in the chip so that we can then come back and find where we put the atoms within the device to make contacts. Those contacts are then made at the same length scale as the atoms and dots.”
Though the team is able to place single atoms of phosphorus, they use clusters of ten to fifty such atoms to make up a so-called register for these application-specific chips. These registers act like quantum dots, preserving quantum properties of the individual atoms. The registers are controlled by a gate voltage from contacts placed atop the chip, and interactions between registers can be tuned by precisely controlling the distances between them.
While the company is also pursuing more traditional quantum computing using this technology, they realized they already had the capacity to do useful simulations in the analog domain by putting thousands of registers on a single chip and measuring global properties, without controlling individual qubits.
“The thing that’s quite unique is we can do that very quickly,” Simmons says. “We put 250,000 of these registers [on a chip] in eight hours, and we can turn a chip design around in a week.”
What to simulate
Back in 2022, the team at Silicon Quantum Computing used a previous version of this same technology to simulate a molecule of polyacetylene. The chemical is made up of carbon atoms with alternating single and double bonds, and, crucially, its conductivity changes drastically depending on whether the chain is cut on a single or double bond. In order to accurately simulate single and double carbon bonds, the team had to control the distances of their registers to sub-nanometer precision. By tuning the gate voltages of each quantum dot, the researchers reproduced the jump in conductivity.
Now, they’ve demonstrated the quantum twin technology on a much larger problem—the metal-insulator transition of a two-dimensional material. Where the polyacetylene molecule required ten registers, the new model used 15,000. The metal-insulator model is important because, in most cases, it cannot be simulated on a classical computer. At the extremes—in the fully metal or fully insulating phase—the physics can be simplified and made accessible to classical computing. But in the murky intermediate regime, the full quantum complexity of each electron plays a role, and the problem is classically intractable. “That is the part which is challenging for classical computing. But we can actually put our system into this regime quite easily,” Gorman says.
The metal-insulator model was a proof of concept. Now, Gorman says, the team can design a quantum twin for almost any two-dimensional problem.
“Now that we’ve demonstrated that the device is behaving as we predict, we’re looking at high-impact issues or outstanding problems,” says Gorman. The team plans to investigate things like unconventional superconductivity, the origins of magnetism, and materials interfaces such as those that occur in batteries.
Although the initial applications will most likely be in the scientific domain, Simmons is hopeful that Quantum Twins will eventually be useful for industrial applications such as drug discovery. “If you look at different drugs, they’re actually very similar to polyacetylene. They’re carbon chains, and they have functional groups. So, understanding how to map it [onto our simulator] is a unique challenge. But that’s definitely an area we’re going to focus on,” she says. “We’re excited at the potential possibilities.”
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