Quantum computing is one of the most exciting emerging technologies right now, and it’s almost certainly an issue that will continue to make headlines for years to come. But there are now so many companies working on quantum computers that it gets really confusing. Who is working on what? What are the pros and cons of each technology? And who do the newcomers have to look out for? That’s what we’ll talk about today.
Quantum computers use units called “quantum bits” or qubits for short. In contrast to normal bits, which can take two values such as 0 and 1, a qubit can take any combination of two values. The magic of the quantum computer arises when you entangle qubits.
Entanglement is a type of correlation, so it ties qubits together, but it’s a correlation that has no equivalent in the non-quantum world. There are a myriad of ways qubits can get tangled, and this creates a computational advantage – when you want to solve certain math problems.
Quantum computers can help, for example, to solve the Schrödinger equation for complicated molecules. This could be used to find out what properties a material has without having to produce it synthetically. Quantum computers can also solve certain logistical problems in optimizing financial systems. So there is real potential for application.
However, quantum computers do not help with all types of calculations, but are special machines. You don’t work alone either, but the quantum parts have to be controlled and read out by a conventional computer. You could say that quantum computers are used to solve problems of what wormholes are to space travel. They may not take you anywhere you want to go, but * if they can get you anywhere you will get there very quickly.
What makes quantum computing so special is what makes it challenging. To use quantum computers, you need to maintain the entanglement between the qubits long enough to actually perform the computation. And quantum effects are very, very sensitive even to the smallest disturbances. Therefore, to be reliable, the quantum computer must work with multiple copies of the information along with an error correction protocol. And to do this error correction you will need more qubits. It is estimated that the number of qubits we need for a quantum computer to perform reliable and useful calculations that a conventional computer cannot do is about a million.
The exact number depends on the type of problem you are trying to solve, the algorithm and the quality of the qubits, etc. However, as a rule of thumb, one million is a good measure to consider. Among them, quantum computers are mainly of academic interest.
Now let’s look at the different types of qubits and how far we are on the way to this million.
1. Superconducting qubits
Superconducting qubits are by far the most widely used and advanced qubits. They are basically small streams on a chip. The two states of the qubit can be realized physically either through the distribution of the charge or through the flow of current.
The great advantage of superconducting qubits is that they can be made using the same techniques that the electronics industry has used for the past 5 decades. These qubits are basically microchips, except that here they have to be cooled to extremely low temperatures of around 10 to 20 milli Kelvin. You need these low temperatures to make the circuits superconducting, otherwise you can’t keep them in these clean two qubit states.
Despite the low temperatures, quantum effects in superconducting qubits disappear extremely quickly. This disappearance of quantum effects is measured in the “decoherence time”, which is currently a few tens of microseconds for superconducting qubits.
Superconducting qubits are the technology used by Google and IBM, as well as a number of smaller companies. In 2019, Google first demonstrated “quantum superiority,” which means that it was doing a task that a conventional computer could not have done in a reasonable amount of time. The processor they used for this was 53 qubits. I specially made a video on the subject. More information can be found here. Google’s claim to hegemony was later discussed by IBM. IBM argued that the computation could indeed have been performed on a conventional supercomputer in a reasonable amount of time, so Google’s claim was a little premature. Maybe it was. Or maybe IBM was just annoyed that they weren’t the first.
IBM’s quantum computers also use superconducting qubits. Their largest currently has 65 qubits and they recently released a roadmap that projects 1000 qubits by 2023. IBM’s smaller quantum computers with 5 and 16 qubits can access the cloud free of charge.
The biggest problem for superconducting qubits is cooling. From a few thousand it becomes difficult to pack all qubits in one cooling system. So it becomes a challenge there.
2. Photonic quantum computing
In photonic quantum computing, the qubits are properties that relate to photons. This can be the presence of a photon itself or the uncertainty in a particular state of the photon. This approach is followed, for example, by the Xanadu company in Toronto. It is also the approach used a few months ago by a group of Chinese researchers who demonstrated quantum superiority for photonic quantum computing.
The biggest advantage of using photons is that they can operate at room temperature and the quantum effects last much longer than with superconducting qubits, typically a few milliseconds, but ideally up to a few hours. This makes photonic quantum computers much cheaper and easier to use. The big disadvantage is that the systems become very large very quickly due to the laser guides and optical components. For example, the Chinese Group’s photonic system covers an entire table top, while superconducting circuits are just tiny chips.
However, the company PsiQuantum claims to have solved the problem and found an approach to photonic quantum computing that can be scaled to one million qubits. How exactly they want to do it, nobody knows, but this is definitely a development to keep an eye on.
3. Ion traps
In ion traps, the qubits are atoms that are missing some electrons and therefore have a net negative charge. You can then trap these ions in electromagnetic fields and move and entangle them with lasers. Such ion traps are comparable in size to the qubit chips. They also have to be cooled, but not quite as much, “only” to temperatures of a few Kelvin.
The biggest player in the trapped ion quantum computer is Honeywell, but start-up IonQ is using the same approach. The advantages of trapped ion computing are longer coherence times than with superconducting qubits – up to a few minutes. The other benefit is that trapped ions can interact with more neighbors than superconducting qubits.
But ion traps also have disadvantages. In particular, they are slower to react than superconducting qubits, and it is more difficult to set many traps on a single chip. However, they have kept up well with superconducting qubits.
Honeywell claims to have the best quantum computer in the world by quantum volume. What the heck is the quantum volume? It is a metric originally introduced by IBM that combines many different factors such as errors, crosstalk, and connectivity. Honeywell reports a quantum volume of 64 and according to their website they too are moving to the cloud next year. The latest model from IonQ contains 32 trapped ions that sit in a chain. They also have a roadmap according to which they can expect quantum superiority by 2025 and solve interesting problems by 2028.
Now what about D-Wave? D-Wave is the only company to date that sells commercially available quantum computers, and they also use superconducting qubits. Your 2020 model has a staggering 5600 qubits.
However, the D-Wave computers cannot be compared with the approaches followed by Google and IBM, as D-Wave uses a completely different calculation strategy. D-Wave computers can be used to solve certain optimization problems defined by the design of the machine, while the technology developed by Google and IBM works well to create a programmable computer that can be applied to all kinds of different problems. Both are interesting, but it compares apples and oranges.
5. Topological quantum computing
Topological quantum computing is the wild card. There is currently no working machine that uses this technique. But the idea is great: In topological quantum computers, information would be stored in conserved properties of “quasiparticles,” which are the collective motions of particles. The great thing about it is that this information is very robust to decoherence.
According to Microsoft, “the advantage is enormous and there is practically no disadvantage.” In 2018, its quantum computer business development director told the BBC that Microsoft would have a “commercially relevant quantum computer” within five years. However, Microsoft had a major setback in February when it had to withdraw a paper demonstrating the existence of the quasi-particles they were going to use. So much for “no disadvantage”.
6. The far field
These were the biggest players, but there are two newcomers worth watching.
The first are semiconducting qubits. They are very similar to superconducting qubits, but here the qubits are either the spin or the charge of individual electrons. The advantage is that the temperature doesn’t have to be that low. Instead of 10 mK, you “only” have to reach a few Kelvin. This approach is currently being followed by researchers at TU Delft in the Netherlands, supported by Intel.
The second is nitrogen vacancy systems, in which the qubits are places in the structure of a carbon crystal where a carbon atom is replaced by nitrogen. The big advantage of this is that both are small and can be operated at room temperature. This is the approach taken by the Hanson lab in Qutech, a few employees at MIT, and a startup in Australia called Quantum Brilliance.
So far there has been no quantum computation demonstration for these two approaches, but they could be very promising.
So this is the status of the quantum computer in early 2021, and I hope this video will help you understand the next quantum computer headlines that are sure to come.
I want to thank Tanuj Kumar for helping with this video.