About a year ago, Honeywell announced that it had entered the quantum computing race with a technology that was different from anything else on the market. The company claimed that because the performance of its qubits was so superior to those of its competitors, its computer could do better on a key quantum computing benchmark than quantum computers with far more qubits.
Now, roughly a year later, the company finally released a paper describing the feat in detail. But in the meantime, the competitive landscape has shifted considerably.
It’s a trap!
In contrast to companies like IBM and Google, Honeywell has decided against using superconducting circuitry and in favor of using a technology called “trapped ions.” In general, these use a single ion as a qubit and manipulate its state using lasers. There are different ways to create ion trap computers, however, and Honeywell’s version is distinct from another on the market, made by a competitor called IonQ (which we’ll come back to).
IonQ uses lasers to perform its operations, and by carefully preparing the light, its computer can perform operations on multiple qubits at the same time. This essentially allows any two qubits in its system to perform a single operation and lets IonQ build up a complicated entangled system. It’s a contrast to the behavior of quantum computers that use superconducting circuits, where each qubit is typically only connected directly to its nearest neighbors.
Honeywell’s approach also allows any two qubits to be connected with each other. But it does so by physically moving ions next to each other, allowing a single pulse of light to strike both of them simultaneously.
This works because Honeywell’s ion traps aren’t made from a static arrangement of magnetic fields. Instead, the fields are generated using 192 electrodes that can all be controlled independently. This allows the device to create locations where the magnetic field varies in strength, leading to the creation of a location where the ion is happier to reside—technically termed a “potential well.” By changing the charge in these electrodes, the potential wells can be made to move up and down the linear device, and the ions will simply move with them.
By merging two potential wells, the ions they contain can be brought together, allowing one operation to simultaneously affect them both. When that is done, the well can be split, taking the ions back to their original location.
What’s new in the paper are some hard performance numbers on how well this all works. Honeywell says that the maximal amount of time needed to transport an ion from one end of the trap to the other is 300 microseconds. Errors in transport—sending a qubit to the wrong location, for example—are detected automatically by the system, allowing the whole thing to be reset and calculations to be picked up from the last point where the machine’s state was read. These errors are also extremely rare. In a series of 10,000,000 operations, a transport failure was detected only three times.
Competition at volume
But that isn’t the last of the performance figures documented here. Honeywell also turned to quantum volume, a measure originally defined by IBM that takes into account the number of qubits, how connected they are, and how well they avoid generating errors instead of the intended outcome. If the system can perform operations involving random pairs of its qubits without error two-thirds of the time, its quantum volume is two raised to the power of the qubit count. Higher error rates lower the quantum volume; more qubits raise it.
In this case, the Honeywell team ran tests with two, three, four, and six of the device’s qubits. All of them successfully cleared the hurdle, with error-free operation typically in the area of 75 percent for the different qubit counts. Given the six qubits, that results in a quantum volume of 64, which, at the time the manuscript was submitted for review, was a record high.
But again, at that time. There’s some good news from Honeywell’s perspective, in that the company has added more qubits without increasing the error rate, bringing itself up to a quantum volume of 512. By comparison, IBM only reached Honeywell’s earlier mark of 64 this past summer using a machine with 27 qubits but a higher rate of errors.
But there’s also the other ion trap computing company, IonQ. Previously, it had been in a similar place to IBM: more qubits, but more errors. However, it managed to roughly triple the qubit count at the same time that it raised its qubit quality to be comparable to Honeywell’s. With low errors and the large boost in qubit count, its quantum volume comes in at over 4 million, which is quite a bit higher than 512. And while it took about a year for Honeywell to add two qubits, at the time of its announcement, IonQ said it expects to double its qubit number to 64 within eight months—which is now less than three months away.
Room for improvement
That said, Honeywell has clearly identified where the bottlenecks reside. One problem is the noise in the voltage generators that feed power into the electrodes that control the ions. Another is spontaneous noise in the system. Clean up either of those and the performance goes up.
In addition, moving the ions around imparts some energy to them, requiring them to be constantly cooled down again while the machine is in operation. To prevent the cooling process from disturbing the qubits, Honeywell traps a second ion from a different element at the same time and cools that, turning it into an energy sponge for its partner. This is a major time sink while the machine is in operation, so boosting its efficiency would speed up operations.
Beyond that, the basic control system scales up linearly—literally, but only up to a point. Add more electrodes in line with the rest and you can simply trap more atoms. The point where this scaling ends is when it takes too long to move an atom from one end of the row to the other if needed. It’s not clear when that point will be reached, but Honeywell is already considering ideas like two-dimensional arrays of traps and transferring ions between devices.
In any case, the publication itself is informative in two ways. It takes what was an excited corporate announcement a year ago and finally provides the details needed to fully appreciate what was done, and with the validation of peer review. But, the fact that the system that was used to generate the results has become badly obsolete in the time it took the paper to get through peer review gives us a real sense of how exciting the field has become.
Nature, 2021. DOI: 10.1038/s41586-021-03318-4 (About DOIs).