Quantum'Jamming' Could Help Unlock the Mysteries of Causality To keep communications secure in a post-quantum world, cryptographers are digging down into the concept of cause and effect. For the past few decades, researchers have understood that quantum computers should eventually be able to crack the widely used codes that secure much of the digital world. To protect against this fate, they've spent years developing new codes that appear to be safe from future safecrackers armed with quantum computers. At the same time, they've also devised ingenious ways to use the rules of quantum mechanics to keep communications secure. But quantum mechanics, just like the "classical" mechanics that preceded it, is just a theory of nature.

Charles Bennett and Gilles Brassard pioneered quantum information theory. Now they've been awarded the highest honor in computer science. Today it's widely acknowledged that the future of computing will involve the quantum realm . Companies like Google, Microsoft, IBM, and a few well-funded startups are frantically building quantum computers and routinely claiming advances that seem to bring this exotic, world-changing technology within reach. In 1979 all of this was unthinkable.

You can never truly empty a box. Suppose you want to empty a box. You remove all its visible contents, pump out any gases, and--applying some science-fiction technology--evacuate any unseeable material such as dark matter. According to quantum mechanics, what's left inside? It sounds like a trick question.

This paper presents a text classification algorithm inspired by the notion of superposition of states in quantum physics.

Researchers at the company are trying to understand their A.I. system's mind--examining its neurons, running it through psychology experiments, and putting it on the therapy couch. It has become increasingly clear that Claude's selfhood, much like our own, is a matter of both neurons and narratives. A large language model is nothing more than a monumental pile of small numbers. It converts words into numbers, runs those numbers through a numerical pinball game, and turns the resulting numbers back into words. Similar piles are part of the furniture of everyday life. Meteorologists use them to predict the weather. Epidemiologists use them to predict the paths of diseases. Among regular people, they do not usually inspire intense feelings. But when these A.I. systems began to predict the path of a sentence--that is, to talk--the reaction was widespread delirium. As a cognitive scientist wrote recently, "For hurricanes or pandemics, this is as rigorous as science gets; for sequences of words, everyone seems to lose their mind." It's hard to blame them. Language is, or rather was, our special thing. We weren't prepared for the arrival of talking machines. Ellie Pavlick, a computer scientist at Brown, has drawn up a taxonomy of our most common responses. There are the "fanboys," who man the hype wires. They believe that large language models are intelligent, maybe even conscious, and prophesy that, before long, they will become superintelligent. The venture capitalist Marc Andreessen has described A.I. as "our alchemy, our Philosopher's Stone--we are literally making sand think." The fanboys' deflationary counterparts are the "curmudgeons," who claim that there's no there, and that only a blockhead would mistake a parlor trick for the soul of the new machine. In the recent book " The AI Con," the linguist Emily Bender and the sociologist Alex Hanna belittle L.L.M.s as "mathy maths," "stochastic parrots," and "a racist pile of linear algebra." But, Pavlick writes, "there is another way to react." It is O.K., she offers, "to not know." What Pavlick means, on the most basic level, is that large language models are black boxes. We don't really understand how they work. We don't know if it makes sense to call them intelligent, or if it will ever make sense to call them conscious. The existence of talking machines--entities that can do many of the things that only we have ever been able to do--throws a lot of other things into question. We refer to our own minds as if they weren't also black boxes.

If your New Year's resolution is to understand quantum computing this year, take a cue from a 9-year-old podcaster talking to some of the biggest minds in the field, says quantum columnist Karmela Padavic-Callaghan Quantum computing seems to pop up in the news pretty often these days. You've probably seen quantum chips gracing your feeds and their odd, steampunk-ish cooling systems in the pages of magazines and newspapers. Politicians and business leaders are peppering their announcements with the word "quantum" more frequently, too. If you're feeling a little confused about it all, it's a good year for a New Year's resolution to finally figure out what quantum computing is all about. This is an ambitious goal, and the timing certainly makes sense.

This report introduces a novel class of reasoning architectures, termed Quantum Circuit Reasoning Models (QCRM), which extend the concept of Variational Quantum Circuits (VQC) from energy minimization and classification tasks to structured logical inference and reasoning. We posit that fundamental quantum mechanical operations, superposition, entanglement, interference, and measurement, naturally map to essential reasoning primitives such as hypothesis branching, constraint propagation, consistency enforcement, and decision making. The resulting framework combines quantum-inspired computation with differentiable optimization, enabling reasoning to emerge as a process of amplitude evolution and interference-driven selection of self-consistent states. We develop the mathematical foundation of QCRM, define its parameterized circuit architecture, and show how logical rules can be encoded as unitary transformations over proposition-qubit states. We further formalize a training objective grounded in classical gradient descent over circuit parameters and discuss simulation-based implementations on classical hardware. Finally, we propose the Quantum Reasoning Layer (QRL) as a differentiable hybrid component for composable reasoning models applicable to scientific, biomedical, and chemical inference domains.

Are we living in a simulation? The idea that we might be living in a simulated reality has worried us for centuries. Thomas Anderson - otherwise known as Neo - is walking up a flight of stairs when he sees a black cat shake itself and walk past a doorway. Then the moment seems to replay before his eyes. Just a touch of déjà vu, he thinks.

We present the results of cognitive tests on conceptual combinations, performed using specific Large Language Models (LLMs) as test subjects. In the first test, performed with ChatGPT and Gemini, we show that Bell's inequalities are significantly violated, which indicates the presence of 'quantum entanglement' in the tested concepts. In the second test, also performed using ChatGPT and Gemini, we instead identify the presence of 'Bose-Einstein statistics', rather than the intuitively expected 'Maxwell-Boltzmann statistics', in the distribution of the words contained in large-size texts. Interestingly, these findings mirror the results previously obtained in both cognitive tests with human participants and information retrieval tests on large corpora. Taken together, they point to the 'systematic emergence of quantum structures in conceptual-linguistic domains', regardless of whether the cognitive agent is human or artificial. Although LLMs are classified as neural networks for historical reasons, we believe that a more essential form of knowledge organization takes place in the distributive semantic structure of vector spaces built on top of the neural network. It is this meaning-bearing structure that lends itself to a phenomenon of evolutionary convergence between human cognition and language, slowly established through biological evolution, and LLM cognition and language, emerging much more rapidly as a result of self-learning and training. We analyze various aspects and examples that contain evidence supporting the above hypothesis. We also advance a unifying framework that explains the pervasive quantum organization of meaning that we identify.

Will quantum be bigger than AI? There's an old adage among tech journalists like me - you can either explain quantum accurately, or in a way that people understand, but you can't do both. That's because quantum mechanics - a strange and partly theoretical branch of physics - is a fiendishly difficult concept to get your head around. It involves tiny particles behaving in weird ways. And this odd activity has opened up the potential of a whole new world of scientific super power. Its mind-boggling complexity is probably a factor in why quantum has ended up with a lower profile than tech's current rockstar - artificial intelligence (AI).