In a laboratory in Broomfield, Colorado, 98 atoms are suspended in mid-air, held in place by electric fields and cooled to temperatures close to absolute zero.

Each atom is far smaller than anything the naked eye could ever see, yet each carries information in a form that has no counterpart in classical physics.

Together, they form Helios, a new quantum computer built by the British-American company Quantinuum. Quantum computers use the power of quantum mechanics, the rules that govern how physics operates at atomic and sub-atomic scales. Those that use Helios’ model of suspended atoms are known as trapped-ion.

A paper published in Nature describes it as a 98-qubit processor with very high accuracy and performance that pushes beyond what can easily be simulated on classical machines. That sounds impressive, but the important question is not simply whether this is a bigger quantum computer (the previous biggest, System Model H2, had 56 qubits). It is whether it is a better one.

Quantum computers are not just faster versions of ordinary computers. The qubits (quantum bits) that they use to process information can exist in quantum states that do not behave like the ones and zeroes of conventional digital technology.

This allows some calculations to be arranged in ways that may eventually outperform even the largest supercomputers. The possible applications are fascinating: new materials, better optimization methods, improved chemistry simulations and new approaches to cryptography.

The difficulty is that qubits are extremely fragile. They are disturbed by temperature variations, imperfect control, unwanted interactions with the environment and, in some systems, even the act of moving information around the device.

For this reason, the race in quantum computing is not only about having more qubits. It is about having more good qubits, controlled accurately enough to perform long and meaningful calculations.

Why it matters

This is why Helios’ result matters. Quantum computing has been promising to change the world for decades, but many announcements still tend to focus on the number of qubits.

This is like judging a race by the number of runners at the starting line. What matters is how many reach the finish, and in what condition. Helios takes both sides of that challenge seriously. Not only is the 98 qubits relatively large; it also reports very low error rates at this scale.

Errors are more common with quantum computers than with classical ones, so error correction is a big challenge in this area.

The Nature paper gives an average error rate for single-qubit gates of about 2.5 in 100,000 for Helios. A quantum gate is the building block of a circuit in quantum computers.

For two-qubit gates in Helios, which are harder and more important for useful computation, the average error rate is about 7.9 in 10,000. This is similar to the best demonstrations of around 5 in 10,000 errors.

Quantum operations are cumulative. A small error in one step may not matter much, but a useful quantum algorithm may require thousands, millions or more operations. Lower error rates mean that more complex calculations become possible before the quantum information falls apart.

Helios’ other notable feature is all-to-all connectivity. In many quantum computers, qubits can interact only with their nearest neighbours, rather like people who can speak only to those sitting next to them. If two distant qubits need to interact, the information must be moved through a chain of intermediate steps. Each extra step adds time and error.

In Helios, any qubit can in principle interact with any other. This is especially valuable for algorithms where the required pattern of interactions does not fit neatly onto a fixed grid.

Quantum railway

The hardware behind this is also interesting. Trapped-ion quantum computers such as Helios use charged atoms as qubits. These ions are held using electric fields and manipulated with laser pulses.

The approach is known for high accuracy, but scaling it up while preserving that accuracy is technically difficult. Helios uses barium ions in what is called a quantum charge-coupled device, or QCCD, architecture. A useful way to picture it is as a tiny quantum railway.

Ions can be stored in memory regions and physically moved into operation zones when the computer programme needs a calculation to be performed using particular qubits. In those operation zones, carefully controlled laser pulses perform the basic steps of a quantum algorithm, known as quantum gates.

These gates change the quantum state of one ion, or link the states of two ions together, allowing the computer to process information. In Helios, a ring-shaped storage area and a junction help route the ions around the device.

This separation of storage, movement and computation is not just smart engineering. It is a sign that quantum computing is becoming more like a full computing system, rather than a collection of impressive laboratory components.

The machine also uses software that can make routing and control decisions while a program is running. In practice, this means deciding which physical ion should represent each qubit, which ions need to be moved into the operation zones, and in what order the quantum gates should be carried out.

This is important for more advanced quantum programs, especially those where later steps may depend on measurements made during the computation.

And the paper reports that Helios can run random quantum circuits that would be extremely difficult to simulate on classical machines. That is an important benchmark, but not the same as having a generally useful quantum computer.

Random circuit sampling tests the power and complexity of the machine; it does not, by itself, solve problems in medicine, climate science or engineering.

So how big an advance is Helios? It is a serious one, because even if it is not the arrival point of a quantum revolution, it brings together scale, accuracy, connectivity and programmability in one machine.

It is a reminder that transformative technologies rarely arrive in a single leap; they are built step by step, atom by atom, until the impossible starts to look engineered.

Domenico Vicinanza is associate professor of intelligent systems and data science, Anglia Ruskin University

This article is republished from The Conversation under a Creative Commons license. Read the original article.