If quantum computers are so powerful, why do they need thousands—or even millions—of qubits just to work? (check the most powerful quantum computers here)
At first glance, this sounds like a contradiction.
We’re often told that quantum computers are insanely fast, capable of solving problems that would take classical computers longer than the age of the universe. In fact, a quantum computer with just a few thousand qubits could, in theory, break the strongest encryption schemes we use today.
So here’s the obvious question:
We already have quantum processors with more than 1,000 qubits. Are we already at the brink of breaking modern cryptography?
The answer is : not yet.
And the reason is errors.
The Core Problem: Qubits Are Noisy
A building block of a quantum computer is a qubit, and it is very fragile.
It constantly interacts with its environment:
stray electromagnetic fields
temperature fluctuations
imperfect control pulses
All of these can disturb its state.
A single qubit, left on its own, is not trustworthy. If we want to build a reliable quantum computer, we must assume that errors are constantly happening—and design the system to survive despite them.
This is where quantum error correction enters the story.
A Familiar Idea: Redundancy Saves the Day
Before diving into the quantum world, let’s recall the example from last episode (read here) where you want to protect 4 bits of information from a single error and to do that you add 3 extra bits. Those extra bits don’t store new information; they store relationships between the original bits.
Now imagine wanting to protect against:
two errors
three errors
or even more
Each time, the number of extra bits grows. And it grows fast.
The more errors you want to tolerate, the more extra bits you must add.
Quantum error correction follows the same philosophy—but with far greater challenges. The entire process is divided into four steps!
1. Encoding: Turning One Qubit into Many
In quantum computing, we don’t trust individual qubits. Instead, we encode the information of one qubit into many qubits. This first step is called encoding.
You start with:
k qubits of actual information
add extra qubits for protection
ending up with n total qubits
The ratio n / k is called the encoding rate.
A good code has a ratio close to 1
A bad (but sometimes necessary) code might have a very high ratio
For example n/k value of 50 means: You need 50,000 physical qubits just to protect 1,000 useful qubits.
After encoding, those hundreds or thousands of physical qubits behave as a single unit called a:
✨ Logical Qubit ✨
This is the qubit your algorithm actually cares about. Here is a nice picture that depicts the idea of a logical qubit made of physical qubits!
When tracking progress in quantum computing, don’t just look at the total qubit count, pay attention to the number of logical qubits too. You can check list of quantum processers here.
2. Logical Operations: When One Qubit Is Actually a Thousand
Now comes a subtle and fascinating problem. We know how to:
flip a qubit
rotate it
apply two-qubit gates
But what happens when one logical qubit is made of hundreds of physical qubits?
If you want to apply a logical NOT gate, do you:
flip all qubits?
flip some of them?
flip them in a specific pattern?
It turns out the answer is: it depends on the encoding scheme.
Here’s an analogy, owning house vs. apartment. If you own an independent house, you can repaint a wall, change wiring, or renovate freely. But if you own a flat in a massive apartment building, even small changes require coordination, permissions, and careful planning.
Logical qubits are like apartments. Operating on them is not just “doing the same thing many times”, it requires carefully designed, coordinated operations that preserve the error-correcting structure.
Designing how to do these operations is one of the hardest problems in quantum computing.
3. Detecting Errors Without Looking Inside
So the next step is how do we detect an error when it happens? In quantum systems, you cannot directly measure qubits without destroying the information.
This is where things get clever. We introduce extra helper qubits, called ancilla qubits.
These ancilla qubits:
start in a known state
interact with the physical qubits inside a logical qubit
are then measured
By examining the ancilla outcomes, we can infer:
whether an error occurred
what kind of error occurred
This process is called syndrome measurement.
It’s like taking an X-ray: you send something into the body, observe how it interacts, and infer what’s happening inside, without ever opening it up.
4. Correction: Knowing the Symptom Isn’t Always Enough
Once a syndrome is measured, the final step is correction. But this isn’t always straightforward.
Sometimes:
different errors produce the same syndrome
the “symptoms” look identical even though the underlying problem differs
Choosing the correct recovery operation is a delicate and deeply mathematical process, often relying on probabilities and decoding algorithms. More on this, in the next episodes.
The Big Picture
Quantum error correction isn’t a single trick—it’s an entire framework made of:
encoding information into many qubits
logical operations that preserve protection
syndrome measurements using ancilla qubits
decoding and correction under uncertainty
This is why today’s 1,000-qubit machines are not yet powerful, general-purpose quantum computers. Most of those qubits will eventually be used just to protect the rest.
This is why efficient quantum error correction is one of the central pillars of building a scalable, reliable quantum computer!
And until we master it, quantum computers will remain fragile..
Bonus Content:
If you are a serious learner and want to learn more about Quantum Error Correction, check out the blog by Arthur Pesah here: https://arthurpesah.me/blog/
Endorsement (not paid):
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