Quantum Boffin? Here is the paper. For the rest of us:
Imagine you’ve just bought the most advanced self-driving car in the world.
It can predict traffic before it happens, park itself, and even make you a coffee.
But sometimes, for no apparent reason, it swerves into a ditch.
Ok so I am not talking about the new Tesla.
That’s the state of quantum computing today—mind-blowingly powerful even at this nascent stage of development, but still incredibly unpredictable.
Quantum computers can solve certain problems exponentially faster than traditional computers, but they’re also fragile.
Like Elon level fragile, the only difference being that Quantum Computers do not require Ketamine, not yet anyway.
Their fundamental building blocks, qubits, are so sensitive that minor temperature changes, electromagnetic interference, or even the simple act of measuring them can throw off calculations.
To make sure these machines actually work as expected, scientists use a process called Quantum Characterisation, Verification, and Validation (QCVV)—essentially, a quality control system for quantum computers.
But QCVV itself is still a work in progress, and how we measure quantum reliability could define the future of the industry.
I have to read a lot of papers in learning this stuff, this one caught my eye, here is why.
QCVV: The Check-Up Quantum Computers Desperately Need
Quantum computers aren’t like classical computers, where we can easily check if a chip is working correctly.
Instead, qubits exist in superposition, and they rely on entanglement, which means their states are linked in ways we can’t observe directly without disrupting them.
As soon as you reach into check up on them everything falls to a big mess.
This makes testing them incredibly challenging.
That’s where QCVV comes in. It breaks down into three main areas:
- Characterisation – Figuring out how qubits behave, how noisy they are, and what types of errors they experience.
- Verification – Checking that the quantum gates (the building blocks of quantum computations) actually perform as expected.
- Validation – Making sure that the whole system produces reliable results consistently.
Without these checks, quantum computers could be spitting out garbage answers, and we wouldn’t even know it.
But while QCVV is crucial, it’s not without problems.
The Problem: QCVV Still Has Gaps
Despite its importance, QCVV is far from perfect. If it doesn’t improve, quantum computing could hit a wall because we don’t quite know how to scale accurate QCVV past 100 QuBits yet.
Here’s what’s holding it back:
1. The Test Environment Problem
Most quantum computers only work in ultra-controlled environments—near absolute zero temperatures, vacuum chambers, and electromagnetic shielding.
Even minor variations can distort performance.
The issue: If different labs use different test setups, we can’t fairly compare quantum systems.
Real-world impact: A bank using quantum computing for risk modelling might choose the wrong hardware simply because test conditions weren’t standardised.
2. Can We Scale QCVV to Large Systems?
Many QCVV techniques require a ridiculous number of measurements.
Quantum tomography, for example, reconstructs a quantum state by measuring it in many ways—but for a system with just 50 qubits, it would need more data than all the atoms on Earth.
That is an exponentially large number of measurements, making it impractical for testing large-scale quantum systems.
The good news is that Quantum itself could help solve this issue, by using quantum algorithms to efficiently reconstruct quantum states with fewer measurements, but we are not there yet.
The issue: We can’t afford to test large-scale quantum computers using current methods.
Real-world impact: Without scalable testing, companies could be deploying large scale faulty quantum systems, leading to financial or scientific disasters.
3. The Noise Assumption Problem
Most QCVV techniques assume quantum errors are random and memoryless (Markovian noise). . Markovian noise means that errors occur randomly and independently at each moment, without being influenced by past errors.
Think of Markovian noise like static on a radio—it randomly crackles, but each crackle is independent of the last.
But real-world quantum errors often linger and accumulate (non-Markovian noise).
Now, non-Markovian noise is like water leaking into a your nicely polished wooden floor—each drop isn’t just a momentary issue; it soaks in, weakens the structure, rots it, and builds up over time, causing long-term damage.
In quantum computing, non-Markovian noise means errors don’t just disappear but instead influence future calculations, making them harder to correct - just like your wonkey wooden floor.
The issue: If we don’t properly account for how errors build up over time, quantum systems might seem reliable in the short term but fail catastrophically in real-world use.
Real-world impact: A pharmaceutical company using quantum computers for drug discovery might invest billions in faulty research based on inaccurate simulations.
4. Hardware Bias: Are We Measuring the Right Things?
Quantum computers come in different forms—superconducting qubits (IBM, Google), trapped ions (IonQ, Honeywell), and photonic qubits (Xanadu). Each has unique error sources, yet many QCVV methods assume all quantum computers are the same.
The issue: Some benchmarking techniques may unintentionally favour one type of quantum hardware over another.
Real-world impact: Investors and businesses might back the wrong technology, setting back commercial quantum computing for years.
5. The Theory vs. Reality Gap
Some QCVV techniques look great on paper but aren’t practical in real-world settings.
The issue: If we rely on overly theoretical methods, we might think a quantum computer is ready for commercial use when it’s not.
Real-world impact: If a quantum encryption-breaking system isn’t properly tested, it could fail at a critical moment, leading to security vulnerabilities worldwide.
The Research That’s Pushing QCVV Forward
Despite these challenges, this research makes significant contributions that could help fix QCVV and bring quantum computing closer to commercial reality.
1. Making QCVV Scalable
Instead of relying on unscalable techniques like quantum tomography, the research focuses on Randomised Benchmarking (RB) and Holistic Benchmarks—methods that don’t require exponential amounts of data.
🔹 Why it matters: These techniques could allow us to test 100+ qubit systems without impossible data requirements.
2. Tackling the Noise Problem
The research recognises that quantum noise isn’t simple and explores techniques like:
- Gate Set Tomography (GST): Accounts for hidden errors in quantum operations.
- Noise Spectroscopy: Helps model non-Markovian errors, making error tracking more accurate.
🔹 Why it matters: These improvements could help prevent subtle but devastating quantum computing failures.
3. Real-World Benchmarks, Not Just Lab Tests
The paper discusses Holistic Benchmarks, which test entire quantum systems rather than just isolated gates.
🔹 Why it matters: These benchmarks can simulate real-world workloads, making them more useful for businesses considering quantum adoption.
4. Preparing for Logical Qubits
Right now, QCVV mostly tests physical qubits, but the real future of quantum computing lies in logical qubits—which use error correction to make quantum systems more stable.
🔹Why it matters: Without this shift, QCVV won’t be ready for fault-tolerant quantum computing.
So, Can We Trust Quantum Computing Yet?
Not fully—but we’re getting there.
Yes, quantum computers are still moody geniuses, and QCVV still has flaws.
But thanks to research like this, we’re getting closer to reliable quantum systems that could change the world.
With better benchmarking, scalable testing, and improved noise modelling, we could see practical quantum applications much sooner than expected.
If we get QCVV right, quantum computing won’t just be a scientific experiment—it’ll be a revolution in everything from finance to medicine to artificial intelligence.
But if we don’t? We could be entering the quantum era with no way to know if our answers are even correct. 🚀
