to be trained on millions of examples so it can infer the rules of the game. Since quantum computers will enable more accurate simulations, that should enable better predictions from models trained on those simulations. Those models, in turn, could help identify other promising bits of chemistry at which to point the quantum computers. “This is the first time that optimisation loop can be closed,” says Ruchir Puri, chief scientist at IBM Research. A second way that quantum computing and AI can work together is to speed each other’s progress. AI systems can help with one of the biggest hurdles to building a powerful quantum computer: dealing with errors. The “quantum bits”, or qubits, from which quantum computers are built are notoriously unreliable. Engineers try to fix that by grouping several physical qubits into a single “logical” one, in which multiple physical qubits check each other’s work. Firms such as Infleqtion, a startup, think they can improve that process with an AI-powered “decoder” that sits alongside the quantum computer and helps handle the error-correction process. Writing software for quantum computers is notoriously hard, and AI could help with that too. Quantum computers can, in turn, enhance AI. One example is a quantum twist on a so-called “reservoir”, which AI researchers use to model complicated systems such as financial markets. “Reservoir computing” lets an AI cheaply model a noisy, complex and fast-moving system by observing its impact on an intermediary. Each time new data is fed in to the reservoir, its state is altered slightly. The outputs of that system reflect the complexity of the inputs it has received, but can be much simpler to model—in the same way the ripples on the surface of a pool reflect the size, shape and speed of stones that have been thrown into it. The approach has analogues in classical computing, but quantum computers can exploit their unique properties to improve this process. SQC, an Australian firm, sells exactly those sorts of quantum reservoir chips. Telstra, a telecoms firm that is both an investor and an early customer, cut training times for its AI models by 90%. But the perception that quantum computing is hard to work with lingers, says Michelle Simmons, SQC’s founder. “We don’t even mention quantum. We just talk about it as an ‘AI accelerator’.” Dr Cox, at IBM, agrees that quantum computers can help with the training AI models, a task presently performed by millions of GPUs in
power-hungry data-centres. “There are some real opportunities right at the heart of how we do AI today to be leveraging quantum computers,” he says. If he is right, the gains will not necessarily come from speed alone. Quantum computers are expensive and finicky things. The delicate quantum states that make them work need to be carefully protected from the outside world. At IBM’s research centre in Yorktown Heights, New York, one of the company’s System Two quantum computers hums in the middle of a room. The quantum computer itself sits in a vat of liquid helium, bringing its temperature down to a whisker above absolute zero. The specialised circuitry used to control it flanks the vat, and a much larger and louder bank of machines sits off to the side to power the cooling system. But unwieldy as the machine is, it is still much smaller and cheaper than the vast data-centres full of GPUs required by top-end AI models. “Instead of gigawatts, we are in the megawatt type of level,” says Jerry Chow, who leads IBM’s quantum programme. For many fields, including AI, the appeal of quantum computing has started to switch: a technology that was once awaited for being faster now also looks attractive because it might be much more efficient. SQC’s own hardware is built to fit in a standard server rack, despite needing to be cooled to -269°C, and uses less than a tenth of the power of a rack full of GPUs. IBM and D-Wave were among several quantum labs in which America’s government invested $2bn in May. Arvind Krishna, IBM’s boss, called it a statement of confidence that the quantum-computing industry was “right around the corner”. The AI sector will be waiting to greet it. ■ This article was downloaded by zlibrary from https://www.economist.com/science-and-technology/2026/07/29/ai-and-quantum- computers-will-be-frenemies
How to spot AI writing How Tom Holland became the world’s highest-grossing young actor The terrifying threat of a genetically engineered plague Bold, brash and divisive: inside the Barstool Sports empire A racist massacre. A white-supremacist coup. A century of lies Border walls are symbols of power—and monuments to delusion
How to spot AI writing Large language models like long words and em-dashes—or do they? Jul 30th 2026 A GHOST WRITER is haunting the English language. The linguistic spectre can turn its hand to prose, poetry, journalese and corporate jargon. It is frightfully versatile: you can get it to mimic Shakespeare’s sonnets or a schlocky beach read; Ernest Hemingway’s taut prose or the office-printer manual. It is frightfully fast, churning out thousands of words a minute. (Hemingway rarely produced as many in a day, and required much more booze.) Wordsmiths are spooked. AI writing is everywhere. It is in your inbox and on your LinkedIn feed. It is all over the internet, drafting more than a third of new websites by one count. Large language models (LLMs) are helping students write essays and probably helping scientists write papers. Some allege AI-generated prose
won the Commonwealth Short Story prize this year, with judges praising its “quiet authority”. (The Commonwealth Foundation denied the claim.) LLMs have stylistic quirks. They are thought to maximise the use of long em-dashes—and the use of words like “maximise”. They like to “deep dive” (and, better yet, “delve”) into the “rich tapestry” of the world. AI writing is not about a single word or phrase, but a rich tapestry of things. Spotting AI texts can be tricky. This is in part because you need evidence beyond a few words or dashes: claiming that a text is by an LLM because it uses the word “delve” is like claiming one is by Jane Austen because it uses “imprudence”. Bots also write in slightly different ways. There is no single style of AI writing, explains Karolina Rudnicka, a linguist at the University of Gdansk in Poland, just as there is no single style of human writing. Writers have idiosyncrasies—Emily Dickinson, for instance, loved em- dashes—and bots may do, too. But there are a few ways to identify LLM-generated text. One is to use detection algorithms that are trained to spot the texture of human or AI prose. Pangram, a leading firm, claims to have 99.98% accuracy. (It has partnered with Substack, a blogging platform, on such a tool.) Detectors, however, are black-box algorithms that can give false positives. They do not give reasons for why they reach their conclusions. Researchers have also tried scouring texts for suspicious words or comparing papers from before and after LLMs were made available to the public. But these approaches have drawbacks too, not least because it is hard to disentangle AI quirks from other language trends. You can discover AI’s hallmarks by comparing the writing of man and machine. To do this you need a baseline that is distinctive and familiar. The Economist turned to prose that we’re sure is human and that readers will recognise: our own. We designed a study to ask top LLMs—OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini and xAI’s Grok—to write versions of our articles without consulting the web. (As a prompt, we gave them the AI-generated summaries that we have experimentally added to some of our articles.)
This gave us a corpus of human and AI creations and we compared them across 55,940 sentences and 1.2m words. To make sure we were detecting AI quirks rather than our own, we also checked the AI texts against journalism from CNN, the New York Times and the Washington Post. Excerpts from hit novels published between 1950 and 2022 offered another test. Our findings are surprising. AI prose is distinguishable by word and punctuation choice as well as sentence and paragraph structure. But its hallmarks are not what you might expect, partly because its writing style has changed with software updates. That does not mean that LLMs are great writers: their prose lacks lucidity and elegance and is often formulaic. So those aspiring to be impressive (human) storytellers should avoid the following peculiarities in their own prose. First, consider words. The vocabulary that bots overuse has changed: they no longer “delve” and there are not as many “tapestries”. Instead they offer a significant number of polysyllables like “significant”, “increasingly” and “consequences”. They use more rare words (“interdependence”, “reindustrialisation”) and scientific lingo (“parameter”, “methodology”) than humans, and are fond of nominalisations (making nouns from verbs, such as
“expansion” from “expand”). All the LLMs in our study use such words, but particularly Gemini and Claude. Much of this language could be described as what George Orwell called “pretentious diction”. He railed against writers who “dress up simple statements” with complicated words and jargon to sound clever. Such pontificating penmen, Orwell observed, also believe that “Latin or Greek words are grander than Saxon ones”. (Bots agree: more Latinate suffixes crop up in their writing than in human texts.) Then look at punctuation. Many believe LLMs stuff their prose with em- dashes, but that is not true after the most recent updates. Today only Claude uses more em-dashes than human writers, with ChatGPT using markedly fewer than any other writer in our study. Humans rejoice—and start using dashes again. A better way to spot AI-generated writing would be to look for texts without much punctuation at all. LLMs are very Joycean about it: they use fewer commas and semicolons than humans (and hardly any parentheses). They use less punctuation in part because they write longer sentences—“and” is their most overused word—and in part because they do not quote experts.
Finally, study the sentence. Bots’ sentences tend to be long; paragraphs are rarely interrupted with short, punchy statements. How dull. When LLMs want to make their sentences more lively, they often reach for a rhetorical device. Their favourites include: “not X but Y”, “not only but also” and the “rule of three”. (Grouping ideas in threes makes them more engaging, as we did just then.) ChatGPT and Claude use more of these constructions per 1,000 sentences than other LLMs and humans.