Today’s most advanced quantum computers can solve certain narrow problems in minutes that would take classical supercomputers thousands of years. But that headline number is misleading on its own. Quantum computers are extraordinarily powerful at specific types of calculations and essentially useless at most everyday computing tasks. Understanding where that power actually applies, and where it doesn’t, is the key to making sense of the hype.
What Makes Quantum Computers Fast
Classical computers process information as bits, each locked into a value of 0 or 1. Quantum computers use qubits, which can exist in a blend of both states simultaneously through a property called superposition. When multiple qubits are entangled, they influence each other instantly, allowing the machine to explore a massive number of possible solutions in parallel rather than checking them one by one.
This parallel exploration is what creates quantum computing’s power advantage. For problems that involve sifting through enormous combinations of possibilities, like simulating molecular behavior or cracking encryption, a quantum computer’s workload scales far more gently than a classical machine’s. A problem that doubles in difficulty for a classical computer might only get slightly harder for a quantum one. That gap compounds fast: calculations that would take a classical supercomputer thousands of years could, in principle, be handled by a quantum computer in seconds.
Where Things Stand Right Now
The largest quantum processors currently in operation have over a thousand qubits. IBM’s Condor chip, introduced in late 2023, contains 1,121 superconducting qubits. But raw qubit count doesn’t translate directly into computing power, because qubits are fragile. They lose their quantum state quickly due to environmental noise, a problem called decoherence, and every operation introduces a small chance of error.
IBM’s more practical chip, Heron, uses 133 qubits but delivers three to five times better performance than earlier 127-qubit processors by virtually eliminating interference between neighboring qubits. In quantum computing, cleaner qubits matter more than more qubits. The real measure of power isn’t how many qubits a chip has, but how many reliable operations you can string together before errors pile up and the answer becomes garbage.
This is where error correction comes in. To produce one reliable “logical” qubit that can sustain long calculations, you need many physical qubits working together to catch and fix errors. The exact ratio depends on the hardware and the error-correction method, but newer techniques are steadily bringing the overhead down. One recent approach achieved the same error protection as older methods while using roughly a quarter of the qubits.
Quantum Supremacy: What It Actually Proved
Google’s Sycamore processor made headlines in 2019 for achieving “quantum supremacy,” completing a specific sampling task in about 200 seconds that the team estimated would take the world’s fastest supercomputer 10,000 years. Critics quickly noted that the task was essentially designed to be easy for a quantum computer and hard for a classical one, with no practical application.
That criticism is fair, but it somewhat misses the point. The demonstration proved that quantum hardware could outperform classical hardware on at least one computational problem. More recent experiments with Sycamore have reinforced this, showing that as the system scales up, classical computers fall further behind on these types of problems rather than catching up.
Real Performance on Useful Problems
The more meaningful question is whether quantum computers can outperform classical machines on problems people actually care about. Early evidence is emerging. A 2024 study published in Science tested a quantum optimization algorithm on a problem that classical computers struggle with as it scales up. The quantum approach’s difficulty grew at a rate of roughly 1.21 to the power of the problem size, compared to 1.34 for the best classical method. Those numbers look close, but because they’re exponential, the gap widens dramatically as problems get larger. For a problem involving hundreds or thousands of variables, that difference translates to orders of magnitude in speed.
This kind of scaling advantage is where quantum computing’s real promise lives. It’s not about being universally faster. It’s about a class of problems where classical computers hit a wall and quantum computers don’t, at least not nearly as quickly.
The Encryption Question
One of the most discussed implications of quantum power is the ability to break modern encryption. Most internet security relies on the difficulty of factoring very large numbers, something a sufficiently powerful quantum computer could do efficiently using a well-known algorithm developed by mathematician Peter Shor in the 1990s.
Breaking a standard 2048-bit RSA key, the kind protecting sensitive communications today, would require a quantum computer far beyond current capabilities. Recent estimates vary depending on the hardware type and how long you’re willing to let it run. Using superconducting qubits with today’s best error rates, you’d need roughly 98,000 to 471,000 physical qubits, depending on whether you allow a month or a single day of computation time. Architectures based on slower but more precise technologies like trapped ions or neutral atoms would need between 1.3 million and 58 million qubits, depending on their accuracy. One prominent estimate for neutral atom systems puts the number at around 13 million physical qubits.
For context, today’s largest chips have about 1,100 qubits. Current quantum computers are nowhere close to breaking encryption, but the gap is narrowing on a timescale of years, not centuries. Governments and tech companies are already transitioning to quantum-resistant encryption standards in anticipation.
What Quantum Computers Can’t Do
Quantum computers will not replace your laptop. They offer no advantage for everyday tasks like browsing the web, running spreadsheets, editing video, or training most current AI models. They’re not generically “faster.” They’re a fundamentally different kind of machine, purpose-built for problems involving massive combinatorial search spaces, quantum simulation, and certain mathematical structures.
They also can’t simply be scaled up by adding more qubits the way you’d add more memory to a server. Each additional qubit increases the engineering challenge of keeping them all stable and entangled. Cooling requirements are extreme: most quantum processors operate near absolute zero, colder than outer space, inside dilution refrigerators the size of a room.
The Road to Practical Power
IBM’s public roadmap offers the clearest timeline for what’s coming. By 2026, the company aims to demonstrate the first example of a genuine scientific advantage over classical computing, along with a working fault-tolerant module. By 2027, the goal is a system capable of running circuits with 10,000 operations across 1,000 or more qubits. The major milestone is 2029: the first fault-tolerant quantum computer available to clients, capable of executing 100 million operations on 200 logical qubits.
That 2029 target is significant. A machine running 100 million gates on 200 error-corrected qubits would be powerful enough to tackle problems in drug discovery, materials science, and financial modeling that are genuinely out of reach for any classical system. It wouldn’t break encryption yet, but it would mark the transition from quantum computing as a research curiosity to quantum computing as a practical tool.
The honest summary: quantum computers are already more powerful than any classical machine on a narrow set of tasks, and they’re roughly a decade away from transforming industries where combinatorial complexity is the bottleneck. For everything else, your laptop remains the better machine.