In a recent Quanta article, What Makes Quantum Computing So Hard to Explain?, Scott Aaronson cautions,
“To understand what quantum computers can do — and what they can’t — avoid falling for overly simple explanations.”
That’s great advice, but it is also kind of vacuous since you could say the same thing about digital computers, or a toaster for that matter. The problem with the explanations of quantum computing as they exist today is not that they are overly simple — it is that they are overly detailed. Google “how does a quantum computer work,” and you are met right out of the gate with qubits and parallel universes, spooky action at a distance, exponential growth, and — wow — no wonder people are confused.
The entire premise of the statement above is that someone wants to know what quantum computers can do — for them. Yet, quantum computer scientists feel compelled to talk about superposition and entanglement. While it is fine to talk about superposition and entanglement — I’ve stopped people on the street to talk about quantum physics — that’s not what people need to hear about quantum computing through casual Google searches. Aaronson himself makes an attempt in the article to explain quantum computing that avoids his own criticism:
“So a qubit is a bit that has a complex number called an amplitude attached to the possibility that it’s 0, and a different amplitude attached to the possibility that it’s 1.”
This is absolutely 100% correct , but I’m going to wager that this means nothing to someone who didn’t already know that. What is going on here? Why do we try so hard to explain every detail of quantum physics as if it is the only path to understanding quantum computation? The answer partially lies in the illusion of explanatory depth. We have this illusion that we understand things we know how to use. But we don’t. Think about it. Do you know how a computer works? A toaster? A doorknob? If you think you do, try to explain it. Try to explain how you would build it. Use pictures if you like, but I think you will quickly change your mind about how much you thought you understood about even simple technology.
We don’t use quantum computers, so we don’t have the illusion we understand how they work. This has two side effects. The first is that we (the quantum scientists) think conventional computing is generally well-understood or needs no explanation. The second is that we (the quantum scientists again) accept the idea that quantum computing is hard to explain. In turn, this causes us to try way too hard at explaining it, hoping the listener will feel as comfortable with quantum computing as they do with their smartphones.
To see why the “try too hard” approach is a problem, consider an analogy. Imagine our curious friend wants to know what a digital computer can do. Apparently, what the quantum computer scientist would do is start talking about bits of information, logical operators, stored-program architectures, and so on, expecting that the listener would easily connect these concepts together and deduce that the UberEats app is possible. But this is, of course, silly. Instead, what you would want to do is say, “Have you ever ordered food using your smartphone? OK. Let’s explore how your intention to get a nice kale salad gets interpreted by the computer on your phone…”
An even better analogy is the other hot deep-tech topic of artificial intelligence. Search for “what is AI,” and most legitimate explainers will state a generically vague answer and then spend most of the words detailing the existing and future applications. The vague answer given is usually something along the following lines — an AI is an autonomous machine that can learn from known examples and makes generalizations that work for unfamiliar examples. Then, the article will go on to say that AI is used in your digital assistant, to recognize faces in photos, to detect spam, and so on. The reader comes away happy that they know — insomuch as anyone with eight minutes of reading can know — what AI can do.
(By the way, if all someone came here for is an eight-minute read about quantum computers, try this instead.)
I suppose, at this point, the current reader is wondering what the current writer’s grand plan is for solving the world’s current quantum education and literacy problems. I’m glad you asked, as it is innovation myself and other colleagues worldwide are in the midst of creating. For me, it all starts with a change in perspective. When I look into the not-too-distant future, I see quantum software developers who have never heard of the words “superposition” and “entanglement” (much like someone writing code today for the next food delivery app doesn’t use the words “transistor” or “NAND gate”). So with that future quantum software developer in mind, I ask myself what their quantum education looks like and, more importantly, how do we get there from here? (No, not wormholes or flux capacitors.)
I would be remiss to exclude the pun of quantum baby steps. But there are also leaps. Quantum Computing for Babies and Quantum Leaps were an attempt to bring quantum computing to ever-younger audiences.
Others have brought new innovations for introducing quantum computing to general adult audiences. For example, Andy Matuschak and Michael Nielsen have created Quantum Country, which is best described as an introductory textbook with interspersed questions that will automatically be reasked based on how often you answer them correctly (spaced repetition for the cognitive science aficionados).
Brilliant — an app that teaches topics through active problem solving — has a Quantum Computing course. Note that it requires a premium membership to fully enjoy.
BLACK OPAL is an app from Q-CTRL that is currently in private beta which includes highly interactive exercises for learning quantum computing.
Quantum Atlas is a multimedia encyclopedia hosted at the Joint Quantum Institute, which is maintained by a large National Science Foundation-funded group of scientists and science journalists.
Seemingly orthogonal to all of that is quantum games, many of which are designed for the purpose of teaching quantum computing. The best-produced example is the somewhat unimaginatively named Quantum Game.
Speaking of games, I recently wrote about how I’ve changed the way I teach quantum computing to undergraduate students through game development.
I would have agreed with almost everything Aaronson said several years ago. (In fact, he and others have been saying the same thing for ten years.) But I’m kind of bored of that narrative. In fact, I would argue that quantum computing is not hard to explain. All we need is a different perspective.
Read More:What Could Make Quantum Computing Easy to Explain?