In a laboratory in Broomfield, Colorado, 98 atoms are suspended in air, held in place by electric fields and cooled to temperatures near absolute zero.
Each atom is much smaller than anything the naked eye can ever see, yet each carries information in a form that has no counterpart in classical physics.
together, they form Heliosa new quantum computer built by the British-American company Quantinuum. Quantum computers use the power of quantum mechanicsthe rules that govern how physics works on the atomic and sub-atomic scale. Those using the suspended atom model of Helios are known as trapped ions.
A letter published in Nature describes it as a 98-qubit processor with very high precision and performance that pushes beyond what can be easily simulated in classical machines. This sounds impressive, but the important question is not simply whether this is a larger quantum computer (the largest previous, System Model H2had 56 qubits). It is if it is better.
Quantum computers are not just faster versions of ordinary computers. of qubit (quantum bit) that they use to process information may exist in quantum states that do not behave like the zeros and zeros of conventional digital technology.
This allows some calculations to be organized in ways that can eventually outpace even the largest supercomputers. The potential applications are fascinating: new materials, better optimization methods, improved chemical simulations, and new approaches to cryptography.
The difficulty is that qubits are extremely fragile. They are disturbed by temperature changes, imperfect control, unwanted interactions with the environment and, in some systems, even the act of moving information around the device.
For this reason, the competition in quantum computing it’s not just about having more qubits. It’s about having more good qubits, controlled with enough precision to perform long and meaningful calculations.
Why does it matter?
That’s why Helios‘The result matters. Quantum computing has promised 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 they complete and in what condition. Helios takes both sides of that challenge seriously. Not only are 98 qubits relatively large; also reports very low error rates on this scale.
Errors are more common with quantum computers than with classical ones, so error correction is a big challenge in this field.
The journal Nature 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 the two-qubit gates on Helios, which are more difficult and important for useful computing, the average error rate is about 7.9 in 10,000. This is similar to the best demos from about 5 to 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 it means that more complex calculations become possible before the quantum information decays.
The other notable feature of Helios is connectivity from all. In many quantum computers, qubits can only interact with their nearest neighbors, rather like people who can only talk to those sitting next to them. If two distant qubits must interact, information must be moved through a chain of intermediate steps. Each additional step adds time and error.
In Helios, any qubit can in principle interact with any other. This is particularly valuable for algorithms where the required pattern of interactions does not fit well into a fixed network.
The quantum railway
The device behind this is also interesting. Quantum computers with trapped ions 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 increasing it while maintaining accuracy is technically difficult. Helios uses barium ions in what is called a charge-coupled quantum deviceor QCCD, architecture. A useful way to picture it is as a little quantum railway.
Ions can be stored in memory regions and physically moved to operating regions when the computer program needs a calculation to be performed using separate qubits. In those operating zones, carefully controlled laser pulses perform the basic steps of a quantum algorithm, known as a quantum gate.
These gates change the quantum state of an ion, or link the states of two ions together, allowing the computer to process the information. In the Helios, a ring-shaped storage area and a junction help guide the ions around the device.
This separation of storage, motion, and compute isn’t just smart engineering. It’s a sign that quantum computing is becoming more like a complete computer system, rather than a collection of impressive laboratory components.
The machine also uses software that can make routing and control decisions while executing a program. In practice, this means deciding which physical ion each qubit should represent, which ions should be moved into the operating regions, and in what order the quantum gates should be performed.
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 quantum random circuits that would be extremely difficult to simulate in classical machines. This 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 of an advance is Helios? It’s serious, because even if it’s not the arrival point of a quantum revolution, it brings together scale, precision, connectivity and programmability in one machine.
It’s a reminder that transformative technologies rarely arrive in a single step; are built step by step, atom by atom, until the impossible begins to look engineered.
Domenico Vicinanza is an associate professor of intelligent systems and data science, Anglia Ruskin University
This article was reprinted from Conversation under a Creative Commons license. Read on original article.
