Why do some people say a quantum computer has 4 bits, and others say they have hundreds? Why is it more complicated than a regular computer? As a scientist in this field myself, I can’t help but feel bad about the confusion surrounding this topic. People are really just curious about the potential of this cool sounding technology, and although news reports continue to promise the appearance of these machines very soon, the exact definition of a quantum computer still seems to be elusive.

I’m going to try and explain one of the main problems in the field of quantum computing today through the use of a visual analogy. The following explanation gets to the heart of a real disagreement, even between experts in the field. There are heated debates in the scientific community over this problem (yes, scientists get into dogfights too). So be aware that you’re getting some juicy inside knowledge here about what really stumps even expert scientists! Of course we don’t often want to shout about the fact that we get stumped by things… but it is true :) I should mention that this explanation is a very conceptual and abstract way to look at quantum computing so there won’t be any implementations or specific algorithms discussed. It compares the ‘conventional’ approach of quantum computing with a new approach, called ‘Natural Quantum Computing’ (NQC). The quantum computers built by D-Wave are of the NQC type.

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**Building blocks – in theory and in practice**

Let’s begin with some background. When engineers and scientists build a new technology, there are two factors that come into play. There’s the theoretical model of the technology or system, and then there’s the practical implementation of that technology or system. These two companions are like yin and yang, both are necessary for the whole to function, yet they are forever competing with one another, two sides of the same coin.

In my analogy, my yin and yang are going to be represented by circles:

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Which circle is better? You might argue that the perfect yin circle is mathematically more beautiful, but if it is never found in nature, then is it really of any use to us? The more natural yang circle has its own bumpy beauty in that it really represents the true nature of what we see around us. You just don’t see perfect circles in nature.

Now, what does this have to do with computing? Well, when we want to build a system, we build it out of bumpy circles (we have to, as they are the only things we can find around us). But we understand it – and predict how it will behave mathematically – by approximating it with perfect circles, because those are easier for us to calculate with. So, let’s pretend that our circles are now building blocks:

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In most cases, this works pretty well. Our mathematical models now predict the behaviour of the circles pretty much the same way as they behave in nature (if you’d like to think about this system ‘behaving’ dynamically, imagine removing one of the circles on the lower layer and trying to predict where they would all end up). Our model of the circles would probably get it roughly right.

When we try to understand classical digital circuits, the same thing happens. We build a ‘model’ of a classical digital computer, like the picture above, and that model behaves in a similar way to its real-world counterpart.

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**Building a ‘conventional’ Quantum Computer using circles**

However, (and here begins the juicy bit) – quantum computers don’t work like that. At least, not the ones that people have been trying to build up to now. I’m going to use the phrase ‘conventional QC’ to describe those, because there IS a different way of doing things which I’ll describe later. It turns out that in a conventional QC that slight difference between the yin and yang descriptions really does make ALL the difference.

What would a conventional quantum computer look like if we used the circle analogy to describe it? Well our mathematical yin model of it would probably look something like this:

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What happens when you try and build this model using real, bumpy, yang circles? Well, you can try…

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Oops! What happened? It didn’t work like we thought it would. The imperfections in the circles just made our delicate system come crashing down. Try again? The same thing happens. You can never get those circles to behave like their yin counterparts. The bumpiness just makes it impossible.

This is exactly what happens when we try to build a quantum computer. Our theories tell us that if we can just get 50, or 100, or maybe 500 of those darned circles to balance on top of one another then our technology will be able to fulfil our every dream! But we just can’t do it in real life – the thing keeps falling over! That is exactly what is meant by a system undergoing ‘decoherence’ if you are familiar with quantum computing parlance.

Well, something has to be done about this. We still want to be able to build a quantum computing system, right?

Yes – but there is an important problem to be addressed here. Who is at fault? Something didn’t add up, and blame must be assigned! Most people blame the yang engineers of the system for using bumpy circles: “Your circles are not perfect enough. Your system falls over. You’re not trying hard enough.”

So the diligent engineers try to find ways to make their circles more and more smooth. Little by little, they are able to balance one or two more on top of each other, for a little longer. But the circles can never be exactly perfect – so although that mathematically stable equilibrium of hundreds of balanced circles can be easily be modelled in theory, it is questionable as to whether it can it ever be built.

The interesting thing is that the question is almost never asked the other way round:

Are the yin circles perhaps too perfect?

In other words: Why do we create models of things that we then can’t ever practically make for real? All approximate models break down at some point, but the difference is usually small enough to still be able to inform our practical building of things. But in this case the model seems useless for anything we try to build that is more complex than a few building blocks!

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**Natural Quantum Computation**

There is another way to build quantum computers that DO behave like their models. There is a type of quantum computing known as Natural Quantum Computing (NQC). This is a way of using quantum systems that we CAN build, in a way that is practical, and doesn’t go against what nature intended to happen to those circles.

A natural quantum computer system would be represented more like this:

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Now the bumpiness doesn’t matter as much, because once again our model behaves a lot more like the real thing we are building. We have attacked the problem from both sides here – we have taken a reality check on our expectations of building with bumpy circles, and we have designed a mathematical model that respects that. We can still build up our quantum computer to be bigger and bigger using this method, and to do more and more, but it now captures the essence of how nature’s elements really do behave. Now this should answer the question about why different people have built quantum computers with different numbers of ‘qubits’ (quantum bits) – try and balance them using the conventional approach, and it depends how well you smooth out your bumpy circles. You might be able to get four, five, or even seven wobbling precariously on top of one another. People are working all over the world on trying to improve what nature has given to us, to get those circles smoother and smoother.

But using Natural QC and you can easily build systems with hundreds of bits. The system works differently, and it doesn’t fall over so much!

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**So why isn’t everyone taking this Natural QC approach?**

Some people feel that it is better to just keep trying to make the circles ever smoother, because people are very familiar with the theoretical ‘yin model’ and have worked with this system mathematically for many years, developing algorithms to allow such a system (once built) to solve problems very quickly. But natural quantum computing is also very good at solving specific types of problems, like those in artificial intelligence and optimization. I myself feel that these NQC-suited problems are more interesting. Given that we also know that it is much easier to build these natural quantum computers, for me it’s really a no-brainer! But some people still like the idea of building the precariously-balancing conventional type of quantum computer. And it’s a matter of opinion about whether or not you believe that it is possible to make those circles smooth enough to get them to balance. But that’s half the fun in science – there are some questions that just haven’t been answered yet!

Even so, I think that we are sometimes a little too biased towards the yin theoretical description of a perfect system, hypnotized by the beauty and simplicity of our mathematical models. And we become disheartened and frustrated when real systems don’t behave like this. But this is not necessarily a problem with nature; it can also be thought of as a problem with our models! Sure, we can make nature behave more like our models by polishing those bumpy circles. But we can also make our models behave more like nature too. We can meet half way, and have the best of both worlds. And personally I’d rather build problem solving things (and models of them!) with natural bumpiness than spend my entire life trying to polish circles to an unachievable smoothness.

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