electrons

Floating electrons on a sea of helium

electrons, liquid helium, Physics, quantum computing, quantum mechanics, qubits, Science / Beth Washington / October 8, 2025

By now, a handful of technologies are leading contenders for producing a useful quantum computer. Companies have used them to build machines with dozens to hundreds of qubits, the error rates are coming down, and they’ve largely shifted from worrying about basic scientific problems to dealing with engineering challenges.

Yet even at this apparently late date in the field’s development, there are companies that are still developing entirely new qubit technologies, betting the company that they have identified something that will let them scale in ways that enable a come-from-behind story. Recently, one of those companies published a paper that describes the physics of their qubit system, which involves lone electrons floating on top of liquid helium.

Trapping single electrons

So how do you get an electron to float on top of helium? To find out, Ars spoke with Johannes Pollanen, the chief scientific officer of EeroQ, the company that accomplished the new work. He said that it’s actually old physics, with the first demonstrations of it having been done half a century ago.

“If you bring a charged particle like an electron near the surface, because the helium is dielectric, it’ll create a small image charge underneath in the liquid,” said Pollanen. “A little positive charge, much weaker than the electron charge, but there’ll be a little positive image there. And then the electron will naturally be bound to its own image. It’ll just see that positive charge and kind of want to move toward it, but it can’t get to it, because the helium is completely chemically inert, there are no free spaces for electrons to go.”

Obviously, to get the helium liquid in the first place requires extremely low temperatures. But it can actually remain liquid up to temperatures of 4 Kelvin, which doesn’t require the extreme refrigeration technologies needed for things like transmons. Those temperatures also provide a natural vacuum, since pretty much anything else will also condense out onto the walls of the container.

Diagrams of a chip showing channels and electrodes, along with an image of the chip itself. — The chip itself, along with diagrams of its organization. The trap is set by the gold electrode on the left. Dark channels allow liquid helium and electrons to flow into and out of the trap. And the bluish electrodes at the top and bottom read the presence of the electrons. Credit: EeroQ

Liquid helium is also a superfluid, meaning it flows without viscosity. This allows it to easily flow up tiny channels cut into the surface of silicon chips that the company used for its experiments. A tungsten filament next to the chip was used to load the surface of the helium with electrons at what you might consider the equivalent of a storage basin.

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Researchers optimize simulations of molecules on quantum computers

The net result is a much faster operation involving far fewer gates. That’s important because errors in quantum hardware increase as a function of both time and the number of operations.

The researchers then used this approach to explore a chemical, Mn₄O₅Ca, that plays a key role in photosynthesis. Using this approach, they showed it’s possible to calculate what’s called the “spin ladder,” or the list of the lowest-energy states the electrons can occupy. The energy differences between these states correspond to the wavelengths of light they can absorb or emit, so this also defines the spectrum of the molecule.

Faster, but not quite fast enough

We’re not quite ready to run this system on today’s quantum computers, as the error rates are still a bit too high. But because the operations needed to run this sort of algorithm can be done so efficiently, the error rates don’t have to come down very much before the system will become viable. The primary determinant of whether it will run into an error is how far down the time dimension you run the simulation, plus the number of measurements of the system you take over that time.

“The algorithm is especially promising for near-term devices having favorable resource requirements quantified by the number of snapshots (sample complexity) and maximum evolution time (coherence) required for accurate spectral computation,” the researchers wrote.

But the work also makes a couple of larger points. The first is that quantum computers are fundamentally unlike other forms of computation we’ve developed. They’re capable of running things that look like traditional algorithms, where operations are performed and a result is determined. But they’re also quantum systems that are growing in complexity with each new generation of hardware, which makes them great at simulating other quantum systems. And there are a number of hard problems involving quantum systems we’d like to solve.

In some ways, we may only be starting to scratch the surface of quantum computers’ potential. Up until quite recently, there were a lot of hypotheticals; it now appears we’re on the cusp of using one for some potentially useful computations. And that means more people will start thinking about clever ways we can solve problems with them—including cases like this, where the hardware would be used in ways its designers might not have even considered.

Nature Physics, 2025. DOI: 10.1038/s41567-024-02738-z (About DOIs).