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For the last twenty-seven episodes we have lived mostly in the world of ideas (check them all out here). We talked about superposition, entanglement, interference, and the mathematics that makes quantum computing so fascinating. But if you walk into a real laboratory, you won’t see equations floating in the air. You will see refrigerators colder than outer space, lasers aligned with obsessive precision, microwave electronics, and racks of control hardware. In other words: machines.

So today, for the first time in this series, let’s step out of theory and into the lab.

The question sounds simple:

How do you actually build a quantum computer?

Unfortunately, the honest answer is: we are still figuring it out.

Scientists have been working on this problem for decades. There are promising approaches, impressive prototypes, and rapidly improving systems—but no universal agreement yet on which technology will ultimately win!

To understand why, we need to start with the most primitive building block.

In a classical computer, the smallest piece of information is a bit.

A bit can be either 0 or 1.

A qubit (quantum bit) is the quantum version of this idea. But unlike a classical bit, a qubit can exist not only as 0 or 1, but also as a superposition of both. In simple terms, you can imagine it as being able to take any value between 0 and 1 until it is measured.

As we have seen in earlier episodes, one helpful way to visualize this is as a spin (check here).

But a mathematical description alone does not give you a machine. Eventually you must ask a practical question:

What physical system will represent these qubits and the information they carry?

Before we solve the quantum problem, it helps to remember how classical computers solved theirs. What does a computer really need in order to exist?

Surprisingly little.

You only need a system that can reliably represent two distinguishable states: 0 and 1.

Once you have that, computation becomes the process of changing those states according to rules.

But good computing hardware needs a few additional properties:

That last point is critical. The true power of computing comes from how many bits you can manipulate and how quickly you can manipulate them.

There is a famous saying that captures this beautifully:

Computers are not intelligent in the human sense. At their core, they are incredibly simple: they just move around 0s and 1s.

But they do it billions of times per second, and they do it with billions of bits at once.

That combination creates the illusion of intelligence.

Okay, all four points are clear but what are the physical systems that can satisfy the above?

Once you realize the four points above, you also realize that it is actually not that difficult to satisfy the first one. Here are some really cool examples of how a computer can be created with everyday objects like marbles, dominos, and even water!

A computer with marbles by The Action Lab:

A computer made from dominos by Spanning Tree:

A computer made from water by Steve Mould:

But most of these approaches fail one key requirement:

scaling.

You might build a water computer that can add two numbers like the one above but building billions of them would be impractical.

That is why one invention changed everything.

The turning point in computing history was the invention of the transistor in 1947.

A transistor is a tiny electronic switch made from semiconductors, most commonly silicon. By controlling voltage at one terminal, you can control whether current flows through another.

In simple terms:

Because transistors are tiny, reliable, and easy to manufacture in enormous numbers, they became the perfect physical embodiment of bits.

This technology eventually allowed engineers to place billions of transistors on a single chip—the modern microprocessor. The global technology hub Silicon Valley even gets its name from this material.

It was one of the few crucial discoveries that changed the face of the world and technology. As expected, it resulted in another Nobel Prize in Physics. Here is the official statement from the Nobel Committee:

For classical computing, the question “what should represent a bit?” has been decisively answered: transistors made of silicon.

What about quantum computers?

Quantum computing faces a similar—but much harder—question.

We know what a qubit is mathematically. But what physical system should we use to implement it?

This is not a trivial question.

A qubit must satisfy many demanding requirements:

Finding a physical system that satisfies all of these simultaneously is extremely difficult. That is why the field currently has multiple competing technologies.

Some of the leading candidates include (we learn about each of them in the next episodes):

Each of these represents qubits in a completely different physical way. Every approach has advantages and disadvantages. Some have extremely high accuracy but are difficult to scale. Others scale well but are harder to stabilize.

And right now, no one knows which one will ultimately dominate the industry. This is the “The Quantum Hardware Race.” Different companies and research groups are betting on different technologies.

In many ways, this moment resembles the early days of classical computing, when engineers experimented with vacuum tubes, relays, mechanical switches, and magnetic cores before transistors finally emerged as the clear winner.

Quantum computing today is still in that experimental era.

If researchers are searching for the best qubit technology, what exactly are they looking for?

There are several key criteria:

These criteria were famously summarized by physicist David DiVincenzo, and they still guide the design of quantum computers today.

Now that we have stepped inside the laboratory, the next question becomes obvious:

How do these different qubit technologies actually work?

In the coming episodes, we will explore all physical systems in detail. Each approach tells a fascinating story about physics, engineering, and the future of computation. For now, the most important takeaway is this:

We understand how quantum computing works in theory. But building a machine that can do it reliably at scale remains one of the great engineering challenges of our time.

And that is exactly where the real adventure begins, stay tuned!