Can a Quantum Processor Beat a Classical Computer at a Real-World Problem?

[Layla86]

This is the question that sits underneath almost every quantum story we post here, so I thought it deserved a proper thread of its own. Every few months a lab announces that its machine has done something no supercomputer could match. A few weeks later, someone usually finds a cleverer classical method and the gap narrows or closes. Meanwhile, investors keep pouring billions into the field on the promise that useful advantage is just around the corner. So where do we stand today, and what would it take to settle the argument?

First, it helps to separate three terms that often get blurred together. Quantum supremacy, a phrase coined by John Preskill in 2012, means a quantum device doing any task faster than any classical computer could, even if the task is useless. Quantum advantage usually means beating classical methods on something people care about, at least in principle. Quantum utility, a term IBM has pushed, means a quantum computer producing reliable results at a scale where classical brute force struggles, even without a proven speed up. A lot of heated arguments online come down to people using these words to mean different things

The modern debate kicked off in 2019, when Google said its 53 qubit Sycamore processor had completed a random circuit sampling task in about 200 seconds. Google estimated the same job would take a leading supercomputer around 10,000 years. IBM quickly argued that, with better use of disk storage, a classical machine could do it in days rather than millennia. Over the following years, improved tensor network methods cut the classical cost dramatically. The experiment was still a landmark, but it showed how fragile these comparisons can be

In 2023 IBM published a paper in Nature using 127 qubits to simulate the dynamics of a quantum magnet, framing it as evidence of utility before full error correction. Within weeks, several groups showed that classical approximation methods could reproduce the results on ordinary hardware. That pattern, a quantum claim followed by a classical counterpunch, has become almost a ritual in the field. D-Wave went through the same cycle in 2025, when its Science paper on simulating magnetic materials was challenged by researchers using tensor networks, and D-Wave then published a rebuttal of its own. None of this means the quantum results were worthless. It means that beating classical computing is a moving target, because classical algorithms keep improving too

The more recent milestones have tried to fix the weaknesses of earlier claims. In October 2025, Google announced its Quantum Echoes algorithm on the Willow chip, published in Nature, which measured a quantity called an out of time order correlator. Google said it ran about 13,000 times faster than the best classical approach on a leading supercomputer, and crucially the result can be checked, either by another quantum computer or against real physical experiments. That word verifiable matters, because random circuit sampling produced outputs that were very hard to check at all. Separately, a JPMorgan Chase and Quantinuum team reported certified randomness generated on a trapped ion machine, a narrow but real task where the quantum output has a provable property

So what counts as a real-world problem? I would suggest four tests. The problem should be one that someone outside the quantum community would pay to solve. The quantum machine should beat the best known classical method, not a weak or outdated one. The answer should be verifiable, so we are not simply trusting the hardware. And the whole process, including setup, error mitigation and reading out the results, should be cheaper or faster end to end than the classical route. By those standards, no demonstration so far clears every bar at once, although several clear two or three of them

The most promising candidates are well known. Chemistry and materials science top most lists, because simulating molecules with strongly interacting electrons is exactly what quantum computers are naturally suited to. Catalysts, battery materials and nitrogen fixation are the examples people keep returning to. The US Department of Energy recently published eight priority science problems for fault tolerant machines, in chemistry, materials, subatomic physics and applied mathematics, and said results will be benchmarked against its best supercomputers. That is the right standard, and it is a high one

Finance and optimisation are more contested. HSBC and IBM reported in 2025 that adding quantum generated features to a bond trading model improved predictions of whether trades would be filled by up to 34% compared with classical methods alone, using an IBM Heron processor. That is a striking number, but some researchers questioned whether the gain came from the quantum part itself or from the way the data was processed. Optimisation problems such as logistics and scheduling are attractive commercially, yet classical heuristics are extremely good at them, and proven quantum speed ups there tend to be modest. I would be surprised if this is where the first undisputed win comes from

Then there is cryptography, which is the reason this forum exists. Shor's algorithm would let a large, error corrected quantum computer break RSA and elliptic curve encryption, a clear real-world problem with an unambiguous answer. In 2025 Craig Gidney at Google estimated that RSA-2048 could be factored in under a week with fewer than a million noisy qubits, a big drop from earlier estimates of around 20 million. Today's machines are nowhere near that scale with the error rates required. But it is a reminder that when advantage does arrive in this area, it will not be a subtle statistical improvement, it will be decisive

That brings us to the real bottleneck, which is error correction. Most of the problems that would transform industries need long, deep calculations, and today's noisy qubits lose their information too quickly. Logical qubits built from many physical ones are starting to appear in labs, and roadmaps from IBM, Google, Quantinuum and others point to fault tolerant systems around the end of this decade. DARPA's Quantum Benchmarking Initiative is explicitly testing whether a machine whose value exceeds its cost can exist by 2033, and its managing director has said he increasingly expects someone to get there. Until then, near term claims will keep living in the grey zone between impressive physics and practical value

My own view is that the honest answer today is not yet, but probably within the next decade for a narrow set of scientific problems. The first clear win is most likely to come from simulating a molecule or material that matters to chemists, verified against experiment, on an error corrected machine. Broad commercial advantage across finance, logistics and AI is much less certain, and some of it may never arrive. The field also needs to resist the temptation to declare victory every time a benchmark is passed. Credibility is a resource, and it is easy to spend

So, over to you. Do you think any result so far counts as a quantum processor beating a classical computer at a real-world problem? Which field do you expect to see the first undisputed win, and roughly when? And should the community agree on a standard test before anyone is allowed to use the word advantage?

Our Quantum Echoes algorithm is a big step toward real-world applications for quantum computingblog.google