Here’s why superposition and entanglement have nothing to do with understanding quantum computers
By: Chris Ferrie
Tags: Deep Technology, Quantum Computing, Computing, Quantum Physics, Explainer

The greater the functionality of a tool, the less efficient it will be for any given task. Take, for example, The Giant, which earns the title of the world’s most multifunctional penknife. It is a Swiss Army knife with 87 tools. It is over 8 inches long and weighs 3 pounds.

So multi-purpose, it has no purpose. Photo by Slartibartfass.

Even if I had a use for every one of those tools, I still would not buy that knife. Now, if it were only the size of a smartphone, that would be a product worth carrying around. The point this knife illustrates is that specialized tasks are best carried out using specialized tools. If you open a hundred bottles of wine per day, for example, this Swiss Army knife has a corkscrew — but you are far better off buying a machine optimized for opening bottles of wine.

Swiss Army computer

A computer is like a Swiss Army knife, but for calculations. A computer can solve all sorts of mathematical problems. And that’s extremely useful because many everyday problems can be phrased as math problems. Obvious examples are determining how much tip to leave at a restaurant, figuring out what time to catch the bus to arrive early for that meeting, adding up the values in a spreadsheet, and so on. Less obvious examples that are really just hidden math problems are recognizing faces in a digital photo, formatting words in a document, and seamlessly showing two people’s faces to each other on other sides of the world in real-time.

The central processing unit, or CPU, inside your tablet, smartphone, or laptop is tasked with carrying out any possible set of instructions thrown at it. But, because it can do anything, it’s not the best at doing specific things. This is where the other PUs come in. Probably the most famous is the GPU, or graphics processing unit.

Maybe graphics aren’t something you think about a lot. But, even to display the text you are reading now on your screen requires coordination of the brightness and color of millions of pixels. That’s not an easy calculation for a CPU. So, GPUs were made as special-purpose electronic devices which do the calculations required to display images really well, and not much else. The CPU outsources those difficult calculations to the GPU, and video gamers rejoice!

The QPU

There’s another kind of calculation involving the multiplication and addition of lots of numbers which is very time consuming for a CPU. This kind of calculation is essential for solving problems in quantum physics, including simulating chemical reactions and other microscopic phenomena. It would be convenient for these kinds of calculations if a quantum processing unit (QPU) were available. And indeed they are! These are confusingly called quantum computers, even though they are chips sent very specific calculations by a CPU.

You won’t find a QPU inside your computer today. This is a technology that is currently being developed by many companies and academic researchers around the world. The prototypes that exist today require a lot of supporting technology, such as refrigerators cooled using liquid helium. So, while QPUs are “small,” the pictures of them you will see show large laboratory equipment surrounding them. (Scroll back up to cover photo for a reminder.)

What will the future QPU in your computer do? First of all, we could not have guessed even 10 years ago what we’d be doing today with the supercomputers we all carry around in our pockets. (Mostly, we are applying digital filters to pictures of ourselves, as it turns out.) So, we probably can’t even conceive of what QPUs will be used for 10 years from now. However, we do have some clues as to industrial and scientific applications.

At the Quantum Algorithm Zoo, 65 problems are currently listed that a QPU could solve more efficiently than a CPU alone. Admittedly, those problems are abstract, but so are the detailed calculations that any processor carries out. The trick is in translating real-world problems into the math problems we know a QPU could be useful for. Not much effort has been put into this challenge simply because QPU didn’t exist until recently, so the incentive wasn’t there. As QPUs start to come online, though, new applications will come swiftly.

Simulate all the things

My favorite and inevitable application of QPUs is the simulation of physics. Physics simulations are ubiquitous. Gamers will know this well. When you think of video games, you should think of virtual worlds. These worlds have physical laws, and the motion of the objects and characters in the world need to be calculated — this is a simulation. Physics needs to be simulated when designing aircraft, bridges, and any other engineered system. Physics is simulated in science, too — entire galaxies have been simulated to understand their formation. But quantum physics has resisted simulation because CPUs are really bad at it.

Once we can simulate quantum physics on QPUs, we’ll be able to simulate chemical interactions to rapidly design new materials and medicines. We might also be able to simulate the physics at the creation of the universe or the center of a black hole, and who knows what we will find there.

The take-away

Now, you may have come here thinking you were supposed to walk away with an understanding of qubits, superposition, entanglement, parallelism, and other quantum magic you’ve read elsewhere. Those are not useful ways for thinking about QPUs unless you plan on studying for several more years to become a quantum scientist or engineer (and even then you shouldn’t be getting your information from blog posts). The basic thing you need to know about QPUs is the same thing you know about GPUs — they are special-purpose calculators which are good at solving a particular kind of mathematical problem.

If at some point you end up with a job title that has the word quantum in it, it will probably be a software job (much like there are 20 software engineers for every 1 computer hardware engineer today). The most challenging problem a Quantum Solutions Engineer might face is in translating the calculations their business currently performs into problems that can be outsourced to a QPU—and quantum entanglement, for example, won’t be relevant for that.