Myth 7: Quantum Computing Is Impossible

Chris Ferrie
10 min readMay 27, 2024

“You insist that there is something a machine cannot do. If you tell me precisely what it is a machine cannot do, then I can always make a machine which will do just that.” — John von Neumann

Quantum computing is not without its detractors. Their arguments come in two flavors. The first style of argument against quantum computing is usually simplified as quantum systems are too complex to control at the level necessary for reliable computation. Recall the exponentially long list of numbers needed to specify a qubit. How could we possibly keep track of and control all those? This is not only defeatist but also naive. Complexity often manifests from simple rules, which we seek to manipulate to build up large quantum devices.

The other argument against quantum computing is that noise and errors will be so unavoidable that quantum computations will break down before answers can be reached. Naysayers need only point to existing quantum computers to illustrate their claim, not to mention the hype, empty promises, and failed predictions of “within the next five years.”

Let’s not dive straight in but take a step back.

What really is a quantum computer?

What is called a quantum computer today seems to be a matter of taste. Consider the fact that my smartphone works by flipping bits encoded in transistors that are only a few dozen atoms thick. There are about a billion of them inside my phone, which could not be made without understanding quantum physics. But my smartphone is not a quantum computer — it’s a digital computer. This is because it encodes bits — digital, classical data — and not qubits, the building blocks of quantum data.

But wait. It’s not that simple. I can indeed encode qubits quite faithfully with the transistors in my smartphone. My smartphone could quite comfortably emulate a perfect quantum computer, provided it had less than 30 qubits or so. When I hijack all the computing resources in my phone to do this, it really is a 30-qubit quantum computer.

But you could argue that in that scenario, the qubits aren’t physical. When I googled “what is the most powerful quantum computer,” the top hit was a press release from a company I won’t name claiming that their quantum computer was the most powerful. The device encoded one qubit of information onto each of six individual atoms isolated in a specialized ion trap sitting on an integrated chip — a marvel of modern engineering, no doubt. In such a device, you can make a correspondence between the information you want to encode and the information needed to describe each atom. Thus, qubits are physical.

But… not quite. Once we have large, reliable quantum computers, the qubits will not be encoded onto individual atoms or any other quantum degrees of freedom, for that matter. Error-correcting codes will be used to encode the qubits virtually to protect them from physical errors. This is part of the reason why simply counting qubits in a device doesn’t reveal its true utility and why one company can claim their device is “more powerful” than competing devices, which might have ten times as many qubits. I could facetiously pick up a pile of dirt and say it is a quantum computer since each dirt atom encodes quantum data. The problem is, I wouldn’t be able to reliably encode it, read it, and certainly not process it. My 30-qubit smartphone, however, does reliably encode quantum data. In fact, by some accepted measures, it is the most powerful quantum computer!

Many companies quote the “quantum volume” of their devices. At the time of writing, a solid 10-qubit device would have a quantum volume of 1024, for example. The quantum volume measures the difficulty a digital computer would face in emulating a given quantum device. It is 2 to the power of the number of qubits (provided that the same number of layered instructions could be carried out without error). Ten qubits that can carry out ten complex instructions without error thus have a quantum volume of ²¹⁰ = 1024. My smartphone can carry out instructions perfectly forever, and so it has a quantum volume of ²³⁰ or about a billion. (This is not surprising because it has 8 billion bytes of RAM, and it takes about 8 bytes to represent a qubit of data.)

Alright, I admit I’m being pedantic. The real reason smartphones aren’t called quantum computers is because they are limited to 30 qubits, and there is no way the technology can scale significantly beyond that. Meanwhile, there is hope for quantum hardware companies that the devices they are building can scale up the number of qubits and the errors down indefinitely.

Maybe they shouldn’t call their devices quantum computers today, though — at least not until they outperform my smartphone. The question then is when will that happen? The nice thing — if you really think this is a problem — is that quantum computing is a divergent technology. Far enough into the future, there will be no ambiguity about whether a computer device is really a quantum computer because there will not be enough atoms in the universe to construct smartphone memory capable of encoding the quantum data the new device can carry. So, one solution is to just hold off on our bickering until that time.

What will it take to get there?

In the early days, quantum computers were built by graduate students in poorly funded physics labs. Ever the industrious types, these physicists constructed prototype devices that were meticulously concocted Rube Goldberg machines as much as they were quantum computers. Exposed cables and duct tape are sufficient to demonstrate the concept but do not feature in any path to scalability. Yet, this exemplifies both the challenge and the hope for quantum computers. Yes, new supporting technologies will need to be developed, but also new supporting technology will be developed because that’s what a bunch of humans collaborating on solving problems tend to do.

Let’s consider an example.

Superconducting qubits are so named because they use superconducting circuits cooled to extremely low temperatures. This is done to maintain their superconducting properties and limit thermal (heat) fluctuations. Each qubit is an “artificial atom” made using the same techniques as conventional microelectronics fabrication. Why use existing infrastructure to build something radically new? Well, you work with what you got. However, this aspect of building quantum computers is not a bottleneck. Indeed, specialized superconducting chip foundries are cropping up to address the specialized needs of quantum technology.

Once constructed, maintaining near-absolute-zero temperatures for many qubits requires advanced and scalable cryogenic technology. These needs have pushed existing providers to innovate, creating bespoke solutions specifically for quantum computer chips. Early examples of superconducting qubits had all of the electronics required to control and read them sitting outside the “fridge.” This was borne out of the necessity of experimentation to separate systems and because conventional electronics weren’t designed for cryogenic temperatures. Today, researchers are designing ways to bring the control and readout logic to the level of the qubits, closing yet another gap in full integration. And so on it will go.

Each company involved in building quantum computers has some vision of how their technology will scale, but look carefully at their roadmaps, and you’ll notice quite a few gaps where “miracles” need to occur. But such breakthroughs are inevitable. History shows us that innovation often comes from unexpected places, such as the discovery that graphene could be produced with a pencil and sticky tape. These moments of serendipity, combined with relentless pursuit and ingenuity, are what will bridge the gaps on the roadmap to scalable quantum computing.

Bootstrapping — the iterative process where each advancement builds upon the last — will play a crucial role in quantum computing’s development. Just as tomorrow’s CPUs are designed using today’s CPUs, quantum computing will likely follow a similar evolutionary trajectory, with each generation of quantum computers facilitating the design of its successor. Take a careful look at the design of any complex system, and you will quickly convince yourself that no one person can comprehend all of it. Such systems are built up over years and decades, the collective effort of many individuals collaborating with the very technology they are trying to scale.

The final coin analogy

Find a coin. Flip it. Did you get heads? Flip it again. Heads. Again. Tails. Again, again, again… HHTHHTTTHHTHHTHHTTHT. Is that what you got? No, of course, you didn’t. That feels obvious. But why?

There are about 1 million different combinations of heads and tails in a sequence of 20 coin flips. The chance that we would get the same string of H’s and T’s is 1 in a million. You might as well play the lottery if you feel that lucky. (You’re not that lucky, by the way — don’t waste your money.)

Now imagine 100 coin flips or maybe a nice round number like 266. With just 266 coin flips, the number of possible sequences of heads and tails is just larger than the number of atoms in the entire universe. Written in plain English the number is 118 quinvigintillion 571 quattuorvigintillion 99 trevigintillion 379 duovigintillion 11 unvigintillion 784 vigintillion 113 novemdecillion 736 octodecillion 688 septendecillion 648 sexdecillion 896 quindecillion 417 quattuordecillion 641 tredecillion 748 duodecillion 464 undecillion 297 decillion 615 nonillion 937 octillion 576 septillion 404 sextillion 566 quintillion 24 quadrillion 103 trillion 44 billion 751 million 294 thousand 464.

So, obviously, we can’t write them all down. What about if we just tried to count them one by one, one each second? We couldn’t do it alone, but what if everyone on Earth helped us? Let’s round up and say there are 10 billion of us. That wouldn’t do it. What if each of those 10 billion people had a computer that could count 10 billion sequences per second instead? Still no. OK, let’s say, for the sake of argument, that there were 10 billion other planets like Earth in the Milky Way, and we got all 10 billion people on each of the 10 billion planets to count 10 billion sequences per second. What? Still no? Alright, fine. What if there were 10 billion galaxies, each with these 10 billion planets? Not yet? Oh, my.

Even if there were 10 billion universes, each of which had 10 billion galaxies, which in turn had 10 billion habitable planets, which happened to have 10 billion people, all of which had 10 billion computers, which could count 10 billion sequences per second, it would still take 100 times the age of all those universes to count the number of possible sequences in just 266 coin flips. If that’s not the most mind-blowing thing you’ve read today…

Why am I telling you all this? The point I want to get across is that humanity’s knack for pattern finding has given us the false impression that life, nature, or the universe is simple. It’s not. It’s actually really complicated. But like a drunk looking for their keys under the lamp post, we only see the simple things because that’s all we can process. The simple things, however, are the exception, not the rule.

Suppose I give you a problem: simulate the outcome of 266 coin tosses. Do you think you could solve it? Maybe you are thinking, well, you just told me that I couldn’t even hope to write down all the possibilities. How could I possibly choose from one of them? Fair. But, then again, you have the coin and 10 minutes to spare. As you solve the problem, you might realize that you are, in fact, a computer. You took an input, you are performing the steps in an algorithm, and you will soon produce an output. You’ve solved the problem.

A problem you definitely could not solve is to simulate 266 coin tosses if the outcome of each toss depended on the outcome of the previous tosses in an arbitrary way as if the coin had a massive memory bank. In that case, you’d have to keep track of all the possibilities, which we just decided was impossible. Well, not impossible, just really, really, really time-consuming. But all the ways that one toss could depend on previous tosses is yet even more difficult to count — in fact, it’s uncountable. One situation where it is not difficult is the one most familiar to us — when each coin toss is completely independent of all previous and future tosses. This seems like the only obvious situation because it is the only one we are familiar with. But we are only familiar with it because it is one we know how to solve.

Life’s generally complicated, but not so if we stay on the narrow paths of simplicity. Computers, deep down in their guts, are making sequences that look like those of coin flips. Computers work by flipping transistors on and off. But your computer will never produce every possible sequence of bits. It stays on the simple path or crashes. There is nothing innately special about your computer that forces it to do this. We never would have built computers that couldn’t solve problems quickly. So computers only work at solving problems that we find can be solved because we are at the steering wheel, forcing them to solve problems that appear effortless.

In quantum computing, it is no different. It can be, in general, very complicated. But we look for problems that are solvable, like flipping quantum coins. We are quantum drunks under the lamp post — we are only looking at stuff that we can shine photons on. A quantum computer will not be an all-powerful device that solves all possible problems by controlling more parameters than there are particles in the universe. It will only solve the problems we designed it to solve because those are the problems that can be solved with limited resources.

We don’t have to track and keep under control all the details of quantum things, just as your digital computer does not need to track all its possible configurations. So next time someone tells you that quantum computing is complicated because there are so many possibilities involved, remind them that all of nature is complicated — the success of science is finding the patches of simplicity. In quantum computing, we know which path to take. It’s still full of debris, and we are smelling flowers and picking the strawberries along the way, so it will take some time — but we’ll get there.



Chris Ferrie

Quantum theorist by day, father by night. Occasionally moonlighting as a author.