Quantum Computing Explained: The Future of Computing

Every computer you have ever used, from the phone in your pocket to the most powerful supercomputer on the planet, works the same fundamental way. It stores information as a series of ones and zeros. Every email you send, every video you stream, every calculation you run, no matter how complex, is ultimately reduced to an enormous sequence of binary digits being flipped between two states: on or off, one or zero.

That system has been spectacularly successful for roughly eighty years. It powers the internet, the global financial system, the AI models changing every industry, and every piece of software you have ever touched.

It also has a limit. There are problems so complex that even if you gave the fastest classical supercomputer in the world a hundred years, it could not solve them. Not because the computer is slow. Because the number of possible combinations it needs to check is so astronomically large that no amount of speed makes the problem tractable.

Quantum computing is the attempt to build a fundamentally different kind of machine, one that does not work with ones and zeros at all, but with something stranger, more powerful, and far more fragile. It is not a faster version of your laptop. It is a different kind of computing entirely, and it has the potential to solve problems that are currently impossible to solve.

What Makes Quantum Computing Different

To understand quantum computing, you need to understand three ideas. They are strange. They are counterintuitive. And they are real.

Superposition. In a classical computer, a bit is either a 0 or a 1. One or the other. Never both. A quantum bit, called a qubit, can be both at the same time. This is called superposition. It does not mean the qubit is secretly a 0 or secretly a 1 and we cannot tell. It means the qubit exists in a genuine combination of both states simultaneously, described by probabilities, until it is measured. The moment you measure it, it collapses into one or the other. But before that measurement, it holds both possibilities.

This is the fundamental difference. A classical bit is a coin lying flat: heads or tails. A qubit is a coin spinning in the air: it is both, in a very real mathematical sense, until it lands.

Entanglement. Two qubits can be linked in a way that has no classical equivalent. When two qubits are entangled, measuring one instantly determines the state of the other, no matter how far apart they are. This is not communication. It is a correlation, but one that is stronger than anything possible in classical physics. Einstein famously called it “spooky action at a distance.”

In a quantum computer, entanglement allows qubits to work together in ways that amplify computational power. Two entangled qubits do not just represent two independent states. They represent four states simultaneously. Three entangled qubits represent eight. Ten represent 1,024. Fifty represent over one quadrillion. The computational space grows exponentially with each qubit added, which is why quantum computers become dramatically more powerful as they scale in ways classical computers do not.

Interference. Quantum computers use interference to guide computation toward the right answer. Just as waves in water can amplify each other (constructive interference) or cancel each other out (destructive interference), the probability waves associated with qubits can be manipulated so that wrong answers cancel out and correct answers reinforce. This is how a quantum computer navigates its enormous computational space without checking every possibility one by one.

Together, superposition, entanglement, and interference allow a quantum computer to explore many possible solutions simultaneously and converge on the right one. A classical computer checks possibilities one at a time. A quantum computer explores them in parallel, guided by the mathematics of quantum mechanics.

Why Classical Computers Cannot Do This

The natural question is: why not just simulate quantum mechanics on a regular computer? If superposition is just math, and we have very fast computers, why not use them?

The answer is scale. A quantum system with 50 qubits requires tracking 2^50 possible states simultaneously. That is over one quadrillion numbers. A system with 100 qubits requires tracking 2^100 states, a number larger than the total number of atoms in the observable universe. No classical computer, no matter how fast, can store or process that many values. The memory requirements alone exceed everything humanity has ever built.

Google’s 105-qubit quantum chip completed a benchmark calculation in approximately five minutes. The same calculation would take the fastest classical supercomputer an estimated 10^25 years. That is ten trillion trillion years. The universe is roughly 13.8 billion years old. The number is not just large. It is incomprehensibly, absurdly large.

That is why quantum computing is not an incremental improvement. It is a different category of capability for certain types of problems.

What Quantum Computers Are Good At

Quantum computers are not better than classical computers at everything. They are dramatically better at specific types of problems, and understanding which types of problems they excel at explains why the technology matters.

Optimisation problems. Finding the best solution among an enormous number of possibilities. The travelling salesperson problem (what is the shortest route that visits every city exactly once?), supply chain optimisation, financial portfolio optimisation, and logistics routing are all optimisation problems. The number of possible solutions grows so fast with each added variable that classical computers can only approximate the answer. Quantum computers can, in theory, find better solutions faster.

Simulation of quantum systems. This is the application Richard Feynman originally proposed when he first suggested quantum computing in 1981. Simulating the behaviour of molecules, chemical reactions, and materials at the quantum level is inherently a quantum-mechanical problem. Classical computers struggle with it because they must approximate quantum behaviour. Quantum computers do not approximate it. They are quantum systems that simulate other quantum systems, making them naturally suited to the task.

This has direct implications for drug discovery (simulating how a drug molecule interacts with a protein), materials science (designing new materials with specific properties), and chemistry (modelling chemical reactions that are too complex for classical simulation).

Cryptography. Most modern encryption relies on the difficulty of factoring very large numbers into their prime components. A classical computer cannot do this efficiently for numbers large enough to be useful for encryption. A sufficiently powerful quantum computer, running an algorithm calledShor’ss algorithm, could. This is why quantum computing is both an opportunity and a cybersecurity threat: it could break the encryption that protects most of the internet’s communication, financial transactions, and classified information.

Machine learning. Quantum machine learning is in its earliest stages, but the theoretical promise is significant. Certain machine learning operations involve matrix computations that could be dramatically accelerated on quantum hardware, potentially enabling training on datasets or within timeframes that are impractical on classical systems.

Search problems. Grover’s algorithm, a quantum search algorithm, can search an unsorted database quadratically faster than any classical algorithm. For a database of one million entries, a classical search requires up to one million steps. Grover’s algorithm requires roughly 1,000.

Where Quantum Computing Is Right Now

The technology is real, it works, and it is further along than most people realise. It is also nowhere near ready for general use. Both things are true simultaneously.

The global quantum computing market reached approximately $1.9 billion in 2025 and is projected to grow to roughly $19 billion by 2035. Over $11 billion in total funding has been invested across nearly 500 rounds. Nearly 70,000 active quantum patents exist globally.

The major players are building at an aggressive pace.

IBM has taken the most systematic approach to scaling. Its Condor processor reached 1,121 qubits. Its Nighthawk processor, unveiled in late 2025, features 120 qubits with improved error rates. IBM’s roadmap targets 4,158 qubits through its multi-chip Kookaburra processor, which links three chips together, and the company aims for fault-tolerant quantum computation by 2029 and quantum-centric supercomputers running one billion gates by 2033.

Google demonstrated quantum supremacy in 2019 when its Sycamore processor solved a problem in 200 seconds that would have taken a classical supercomputer an estimated 10,000 years. Its Willow chip, with 105 qubits, achieved the benchmark described above (five minutes vs 10^25 years classically). Google has also proven that increasing the number of physical qubits in an error-correcting code actually decreases the overall error rate, crossing a threshold that researchers have pursued for decades.

Microsoft took a fundamentally different approach with its Majorana 1 processor, the first quantum processing unit powered by what it calls a topological core. Topological qubits are theoretically more stable and resistant to errors than conventional superconducting qubits, though the technology is earlier in its development. Microsoft’s goal is a chip that can scale to one million qubits.

Other players include Atom Computing (1,225-qubit neutral atom system), PsiQuantum (photonic approach, over $1.3 billion in funding), Quantinuum (trapped ion approach, formed from the merger of Honeywell Quantum Solutions and Cambridge Quantum), Amazon (bosonic code research), and dozens of startups across multiple hardware approaches.

The Error Problem

Here is the hardest truth about quantum computing: qubits are extraordinarily fragile.

A qubit must be isolated from virtually all external influence to maintain its quantum state. Any interaction with the environment, heat, vibration, electromagnetic radiation, even cosmic rays, can cause the qubit to lose its quantum properties. This is called decoherence, and it is the central engineering challenge of the field.

Most quantum computers today operate at temperatures near absolute zero, roughly negative 273 degrees Celsius, colder than outer space. The cooling systems required to maintain these temperatures are massive, expensive, and energy-intensive. A quantum computer does not look like a laptop. It looks like a chandelier of gold-plated wires hanging inside a room-sized refrigeration unit.

Even at those extreme temperatures, qubits still accumulate errors. The error rate for current two-qubit operations is around 0.1- 1%, which sounds small but is enormous by computing standards. A classical computer makes roughly one error per billion operations. A quantum computer makes errors constantly.

The solution is quantum error correction: using multiple physical qubits to create a single “logical” qubit that can detect and fix its own errors. The problem is that error correction is expensive. Current estimates suggest you need somewhere between 10 and 100 physical qubits to create one reliable logical qubit. A quantum computer with 1,000 physical qubits might have only 10-100 usable logical qubits after error correction.

This is why raw qubit counts, the numbers companies announce in press releases, are misleading without context. A 1,000-qubit machine is not necessarily more useful than a 100-qubit machine if the error rates are higher. What matters is the number of reliable logical qubits, and that number is still very small.

The good news: error rates have dropped below the 1% threshold for two-qubit gates across all major platforms, making quantum error correction viable for the first time. Google’s demonstration that adding more physical qubits to a surface code actually reduces the logical error rate was a breakthrough precisely because it proved the fundamental concept works. You can build a reliable computer from unreliable parts.

The bad news: we are still years away from having enough error-corrected logical qubits to solve commercially meaningful problems that classical computers cannot. The gap between “this works in principle” and “this solves a real-world problem better than a classical supercomputer” is where the entire industry sits right now.

The Encryption Question

Quantum computing’s most discussed practical implication is its potential to break modern encryption, and this deserves honest treatment because the situation is more nuanced than headlines suggest.

RSA encryption, which secures most internet communication, relies on the mathematical difficulty of factoring large numbers. A sufficiently powerful quantum computer running Shor’s algorithm could efficiently factor these numbers, rendering RSA encryption breakable.

The qualifier “sufficiently powerful” is doing a lot of work in that sentence. Breaking RSA-2048, the encryption standard used by most of the internet, would require roughly 4,000 error-corrected logical qubits. Given the current error-correction overhead, that translates to millions of physical qubits. No quantum computer anywhere in the world has more than about 1,200 physical qubits. The gap between where the technology is and where it would need to be to break encryption is measured in orders of magnitude.

That does not mean the threat is irrelevant. The concern is “harvest now, decrypt later”: adversaries could be collecting encrypted data today with the intention of decrypting it in the future when quantum computers become powerful enough. Sensitive data that must remain confidential for decades (government communications, medical records, financial data, intellectual property) is at risk,k even though decryption capabilities do not yet exist.

This is why the transition to post-quantum cryptography, encryption algorithms that are resistant to quantum attacks, is already underway. The US National Institute of Standards and Technology (NIST) finalised its first post-quantum cryptographic standards in 2024. Major technology companies and government agencies are beginning to implement these new standards, a process that will take years to complete across the global internet infrastructure.

The honest assessment: quantum computers cannot break modern encryption today, and they are unlikely to be able to for at least a decade, possibly longer. But the migration to quantum-resistant encryption needs to start now because the transition itself will take years, and the adversaries collecting encrypted data today are not waiting.

What Quantum Computing Will Not Do

It will not replace your laptop. Quantum computers are not faster versions of classical computers. They are fundamentally different machines that excel at specific problem types and are terrible at others. Browsing the web, writing a document, editing a video, running a spreadsheet: none of these benefit from quantum computing. Classical computers will continue to handle everyday computing tasks indefinitely.

It will not solve every hard problem. Quantum speedups exist for specific classes of problems: optimisation, simulation, factoring, and search. Many computationally hard problems do not have known quantum algorithms that provide a meaningful advantage. Quantum computing is a powerful new tool, not a universal problem solver.

It will not arrive all at once. The transition from experimental quantum systems to commercially useful ones will be gradual. Different applications will become viable at different qubit counts and error rates. Early commercial applications are likely to be in chemistry simulation and optimisation, where even modest quantum advantages can deliver significant value. General-purpose quantum computing is much further away.

Why It Matters Anyway

The reason quantum computing matters, despite being years from broad commercial viability, is the same reason the earliest computers mattered in the 1940s: the problems it will eventually solve are the ones that currently have no solution at all.

Designing a new drug currently requires testing millions of molecular combinations through expensive, time-consuming laboratory experiments. A quantum computer that can simulate molecular interactions directly could reduce that process from years to weeks.

Modelling climate systems requires simplifying the physics to fit within classical computing constraints. Quantum simulation could model atmospheric and ocean-ocean interactions with such fidelity that it yields fundamentally more accurate predictions.

Optimising a global supply chain with thousands of variables, real-time constraints, and constantly changing conditions is a problem that classical computers can only approximate. Quantum optimisation could find solutions that are not just better approximations but genuinely optimal.

These are not incremental improvements. They are capabilities that do not currently exist, and their absence is a bottleneck to progress in medicine, energy, logistics, and science.

The global quantum computing market is small compared to the classical computing market. Still, the investment trajectory reflects a consensus across governments, corporations, and research institutions that the technology will eventually deliver on its promise. The timeline is uncertain. The destination is not.

Quantum computing is not the next upgrade to the computer on your desk. It is a fundamentally different way of processing information, built on the strangest properties of physics, targeting the hardest problems that exist. It is not ready yet. But the parts are being assembled, the errors are being corrected, and the problems it will eventually solve are waiting.