The US- and UK-based company Quantinuum today unveiled Helios, its third-generation quantum computer, which includes expanded computing power and error correction capability.

Like all other existing quantum computers, Helios is not powerful enough to execute the industry’s dream money-making algorithms, such as those that would be useful for materials discovery or financial modeling. But Quantinuum’s machines, which use individual ions as qubits, could be easier to scale up than quantum computers that use superconducting circuits as qubits, such as Google’s and IBM’s.

“Helios is an important proof point in our road map about how we’ll scale to larger physical systems,” says Jennifer Strabley, vice president at Quantinuum, which formed in 2021 from the merger of Honeywell Quantum Solutions and Cambridge Quantum. Honeywell remains Quantinuum’s majority owner.

Located at Quantinuum’s facility in Colorado, Helios comprises a myriad of components, including mirrors, lasers, and optical fiber. Its core is a thumbnail-size chip containing the barium ions that serve as the qubits, which perform the actual computing. Helios computes with 98 barium ions at a time; its predecessor, H2, used 56 ytterbium qubits. The barium ions are an upgrade, as they have proven easier to control than ytterbium. These components all sit within a chamber that is cooled to about 15 Kelvin (-432.67 ℉), on top of an optical table. Users can access the computer by logging in remotely over the cloud.

Helios encodes information in the ions’ quantum states, which can represent not only 0s and 1s, like the bits in classical computing, but probabilistic combinations of both, known as superpositions. A hallmark of quantum computing, these superposition states are akin to the state of a coin flipping in the air—neither heads nor tails, but some probability of both.

Quantum computing exploits the unique mathematics of quantum-mechanical objects like ions to perform computations. Proponents of the technology believe this should enable commercially useful applications, such as highly accurate chemistry simulations for the development of batteries or better optimization algorithms for logistics and finance.

In the last decade, researchers at companies and academic institutions worldwide have incrementally developed the technology with billions of dollars of private and public funding. Still, quantum computing is in an awkward teenage phase. It’s unclear when it will bring profitable applications. Of late, developers have focused on scaling up the machines.

A key challenge to making a more powerful quantum computer is implementing error correction. Like all computers, quantum computers occasionally make mistakes. Classical computers correct these errors by storing information redundantly. Owing to quirks of quantum mechanics, quantum computers can’t do this and require special correction techniques.

Quantum error correction involves storing a single unit of information in multiple qubits rather than in a single qubit. The exact methods vary depending on the specific hardware of the quantum computer, with some machines requiring more qubits per unit of information than others. The industry refers to an error-corrected unit of quantum information as a “logical qubit.” Helios needs two ions, or “physical qubits,” to create one logical qubit.

This is fewer physical qubits than needed in recent quantum computers made of superconducting circuits. In 2024, Google used 105 physical qubits to create a logical qubit. This year, IBM used 12 physical qubits per single logical qubit, and Amazon Web Services used nine physical qubits to produce a single logical qubit. All three companies use variations of superconducting circuits as qubits.

Helios is noteworthy for its qubits’ precision, says Rajibul Islam, a physicist at the University of Waterloo in Canada, who is not affiliated with Quantinuum. The computer’s qubit error rates are low to begin with, which means it doesn’t need to devote as much of its hardware to error correction. Quantinuum had pairs of qubits interact in an operation known as entanglement and found that they behaved as expected 99.921% of the time. “To the best of my knowledge, no other platform is at this level,” says Islam.

This advantage comes from a design property of ions. Unlike superconducting circuits, which are affixed to the surface of a quantum computing chip, ions on Quantinuum’s Helios chip can be shuffled around. Because the ions can move, they can interact with every other ion in the computer, a capacity known as “all-to-all connectivity.” This connectivity allows for error correction approaches that use fewer physical qubits. In contrast, superconducting qubits can only interact with their direct neighbors, so a computation between two non-adjacent qubits requires several intermediate steps involving the qubits in between. “It’s becoming increasingly more apparent how important all-to-all-connectivity is for these high-performing systems,” says Strabley.

Still, it’s not clear what type of qubit will win in the long run. Each type has design benefits that could ultimately make it easier to scale. Ions (which are used by the US-based startup IonQ as well as Quantinuum) offer an advantage because they produce relatively few errors, says Islam: “Even with fewer physical qubits, you can do more.” However, it’s easier to manufacture superconducting qubits. And qubits made of neutral atoms, such as the quantum computers built by the Boston-based startup QuEra, are “easier to trap” than ions, he says.

Besides increasing the number of qubits on its chip, another notable achievement for Quantinuum is that it demonstrated error correction “on the fly,” says David Hayes, the company’s director of computational theory and design, That’s a new capability for its machines. Nvidia GPUs were used to identify errors in the qubits in parallel. Hayes thinks that GPUs are more effective for error correction than chips known as FPGAs, also used in the industry.

Quantinuum has used its computers to investigate the basic physics of magnetism and superconductivity. Earlier this year, it reported simulating a magnet on H2, Quantinuum’s predecessor, with the claim that it “rivals the best classical approaches in expanding our understanding of magnetism.” Along with announcing the introduction of Helios, the company has used the machine to simulate the behavior of electrons in a high-temperature superconductor.

“These aren’t contrived problems,” says Hayes. “These are problems that the Department of Energy, for example, is very interested in.”

Quantinuum plans to build another version of Helios in its facility in Minnesota. It has already begun to build a prototype for a fourth-generation computer, Sol, which it plans to deliver in 2027, with 192 physical qubits. Then, in 2029, the company hopes to release Apollo, which it says will have thousands of physical qubits and should be “fully fault tolerant,” or able to implement error correction at a large scale.