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Scientists discover new way to control magnetic waves for future quantum technologies
Researchers at the U.S. Department of Energy's (DOE) Ames National Laboratory have discovered a way to control the handedness of magnons, tiny magnetic waves that can carry information through materials. ...
The Wilson Science & Technology Forum at The Jenks Center in Winchester
Walter Hubbard experience with FSD vehicles and Olivia Lanes of IBM speaks about Quantum Computers
Learn About Quantum Networks at PubSci Science Cafe Nov. 18
Learn about quantum networks at the next PubSci, a science café presented by Brookhaven National Laboratory, on Wednesday, November 18 in Smithtown.
Lasers and neutrons reveal long-lived quantum state in magnetic material at ORNL
Researchers used laser pulses and neutron scattering to observe a long-lived magnon state that violates detailed balance.
PSI is trying to make a business out of AI-driven scientific discovery
PSI used an unreleased Anthropic model to answer a fundamental question about quantum computing that scientists have pursued for nearly two decades.
QCi Reports $9.5 Million in Quarterly Revenue as Acquisitions Expand Commercial Business
Quantum Computing Inc. reported third-quarter revenue of $9.5 million, exceeding its first-half total, and ended September.
Could Jupiter’s Electric Skies Be Alive? Science Needs A Harder Test
Jupiter’s plasma can organize itself into astonishing structures. AI, archival spacecraft data and experimental quantum sensors could help test whether any
The fastest levitated spinning magnet ever made might help physicists search for dark matter
Tiny levitated magnets are able to avoid contact friction, making them useful for detecting extremely weak forces and magnetic fields and acting as ultralow-noise quantum sensors and precision measurement ...
A Quantum Computer Is Now On Display At Busan’s National Science Museum
A quantum computer is now on display at Busan’s National Science Museum, part of the new “Quantum Magic School” program.
Hong Kong University Of Science And Technology Team Achieves 300x Spin Lifetime Boost In Perovskites
Hong Kong University of Science and Technology (HKUST) researchers boost spin lifetime in perovskites 300x at room temperature.
A quantum breakthrough makes light behave like both a solid and a liquid
Scientists have observed a bizarre quantum state of matter that combines the structure of a crystal with the frictionless flow of a liquid, all at room temperature. The breakthrough overturns a major practical limitation of exotic quantum experiments and could help bring powerful new light-based computing and sensing technologies closer to reality.
Finally! Physicists Create an Exotic Quantum State of Matter That Works at Room Temperature
It behaves like a liquid and a crystal.
Caltech and the New California Quantum Ecosystem
Daily Newsmagazine and City Guide to Pasadena, California featuring local news, breaking news, events, weather, sports news, schools news, shopping, restaurants and more from Pasadena Now
A hidden limit of space and time could unlock electronics 100,000 times faster
Scientists have discovered a surprising quantum limit that prevents electrons from being tracked with unlimited precision in both space and time. The breakthrough could pave the way for manipulating chemical reactions and developing electronics hundreds of thousands of times faster than today's technology.
Unreleased Anthropic model helps solve major quantum-computing problem
An unreleased Anthropic model helped solve a longstanding problem in quantum computing — another major math problem solved with AI.
European scientists spot new quantum state of matter at room temperature
Scientists have observed a new quantum state of matter called a supersolid in an exciton-polariton Bose-Einstein condensate, which is electrically tunable and is seen at room temperature.
Scientists achieve record-breaking low temperature on China's space station, paving the way for new physics experiments
Matter starts to behave differently at extremely low temperatures, creating conditions ideal for studying quantum physics and new states of matter. Now, researchers from China have achieved a new low-temperature ...
Oak Ridge National Laboratory, IBM And AMD Launch New Co-Design Platform for Quantum Error Correction
IBM, Oak Ridge National Laboratory’s Quantum Science Center and AMD unveiled a joint platform integrating quantum and HPC.
The Universe Should Be Empty. A Single Trapped Antiparticle Might Finally Reveal Why It Isn’t.
Scientists chilled a single positron to its quantum ground state, chasing physics’ biggest mystery.
The Quantum World Is Bursting With Chaos—Scientists Just Found What's Holding It Together
By mixing classical nudges with quantum circuits, scientists found rhythms hiding in many-body chaos that could help build better quantum computers.
China’s Photonic Quantum Computer Among WIC Award Winners
China’s Jiuzhang 4.0 photonic quantum computer was among 20 projects honored with the 2026 World Internet Conference.
A tiny squeeze dramatically changes the magnetism of exotic materials
Scientists have discovered that a simple mechanical squeeze can dramatically alter the magnetic properties and electrical behavior of exotic materials called altermagnets. The surprising breakthrough could open the door to faster, more energy-efficient electronics and new technologies for quantum computing.
SLAC scientists find conclusive proof of a mysterious third type of magnetism
For nearly 90 years, scientists believed magnetism fell into just two categories, but researchers have now confirmed a remarkable third type called altermagnetism. Using specialized X-rays, they uncovered a unique magnetic signature that proves these materials behave differently from conventional magnets. Altermagnets combine stability with the ability to efficiently carry information, potentially overcoming major limitations in today's electronics. The discovery could lead to faster computers, more efficient data centers, and breakthroughs in quantum technology.
Powering up for a pivotal moment
Utilizing waste carbon at scale, assessing public trust in AI and data centers, and building a quantum toolkit for critical applications were among the topics energy experts explored at the MIT Energy Initiative’s annual research conference on Sept. 22-23.
Japan Just Powered Up a Quantum Computer Built From Single Atoms—and It May Not Need a Giant Freezer to Run
Just how big could the quantum leap really be?
The Missing Engine Behind Quantum Computing Is Literally All Around Us
Scientists say they can corral up to 40 quantum-ready photons from everyday light, even at room temperature.
'Jiuzhang 4.0' wins leading science and technology award at 2026 WIC
A total of 20 projects, including the "Jiuzhang 4.0" programmable photonic quantum computing prototype, won the 2026 World Internet Conference (WIC) Leading Science and Technology Awards at a ceremony held on Monday in Wuzhen, east China's Zhejiang Province.
Oxford Science Enterprises targets UK’s first trillion-pound deep-tech company
Recent landmark exits in quantum computing and biotech show that the OSE’s model of building companies around university research, recruiting entrepreneurial leaders and providing patient capital produces results. The next step is taking the model global.
Scientists Just Found Where the Quantum World and Our World Meet: Free Fall
Tom Petty’s famous song applies to all of us, because of Einstein’s equivalence principle of gravity in free fall.
Scientists Just Found Where the Quantum World and Our World Meet: Free Fall
Tom Petty’s famous song applies to all of us, because of Einstein’s equivalence principle of gravity in free fall.
Exclusive | Asian scientists are looking to South Korea as it becomes harder to stay in the US
Seoul university says it’s luring researchers in energy, quantum materials and biomedicine while still working with US and Chinese partners.
Adobe vs. International Business Machines: Which Technology Stock Is a Better Buy in 2026?
IBM is growing steadily with software leading the way and a major quantum computing investment on the horizon. Adobe faces serious questions about whether AI will erode Creative Cloud’s dominance despite continued strong cash flow.
Einstein's equations may explain the universe without dark energy
Mathematicians have uncovered what they argue is a fundamental flaw in the standard model of the universe, potentially challenging the need for dark energy. Their calculations suggest that the mathematical framework used to describe cosmic expansion is inherently unstable and that the universe could accelerate naturally under Einstein's original theory of gravity. The findings also raise surprising questions about whether our location in the universe is as ordinary as scientists have long assumed.
A golden age of science is coming
It’s almost hard to remember, but just a few years ago, a lot of very smart people thought that science was in terminal decline. Respected researchers were writing papers with titles like “Are Ideas Getting Harder to Find?”, “Papers and patents are becoming less disruptive over time”, and “Slowed canonical progress in large fields of science”. In 1996, science writer John Horgan even published a book called The End Of Science: Facing The Limits Of Knowledge In The Twilight Of The Scientific Age. The fundamental idea was that the Universe only has so many insights to discover, and that we’ve picked the low-hanging fruit. It was the idea that Joseph-Louis Lagrange expressed when he said that “we cannot find more than once a system of the world to establish.” As the big ideas and the simple ideas get mined out, we’re forced to throw ever more brains at the mysteries of nature just to keep scientific progress going at a fraction of its old pace: It looked like science was hitting a wall. Then along came AI. It’s far too early to tell how much AI will eventually speed up the growth of total factor productivity. But already we can see signs of a speedup in science. Anthropic Founder Jack Clark assembled some of the hopeful data points in a recent lecture: [ Dror Poleg@drorpoleg "If you pick different parts of science, you see the same pattern show up: Progress is accelerating, and it has started to accelerate in the last two years." Jack Clark @jackclarkSF I’m speaking at the IMF in an hour about the singularity and why I believe it has begun and the choices we'll need to make. Watch live here: https://t.co/Hj9HzcXx5h 6:08 PM · Oct 18, 2026 · 21.2K Views 2 Replies · 10 Reposts · 123 Likes ](https://x.com/drorpoleg/status/2111882070376980529) Anthropic is accelerating its investment into drug discovery, and is already announcing some impressive preliminary results: [ BioTender@Junioryu136689 BREAKING: Anthropic unveils early results from six Claude-powered drug development collaborations. • 70% shorter CAR-T signaling domain • ~35,000 antibody designs • >10× tighter antibody binding • 89% less batch-record review time Still largely preclinical. Anthropic @AnthropicAI Over the past few months, Anthropic scientists have engaged in deep research partnerships with six organizations developing new medicines. We've also begun drug discovery efforts of our own. In doing so, we’ve learned much more about how Claude can support the many stages of 9:43 PM · Oct 27, 2026 · 11.2K Views 3 Replies · 24 Reposts · 205 Likes ](https://x.com/Junioryu136689/status/2115197821150495150) Claude also appears to be enabling rapid progress toward a treatment for food allergies. OpenAI, meanwhile, announced a possible breakthrough treatment for neurodegenerative disease. Meanwhile, Google Cloud Chief Scientist Pushmeet Kohli has a list of recent scientific advances that have been enabled by its new Gemini model, including breakthroughs in nuclear fusion reactor design, rare disease research, and quantum computing: [ Pushmeet Kohli@pushmeet Gemini 4 Argon is a step change in model performance. In recent weeks, it has had a transformative impact on the Science projects in our team at @GoogleDeepMind and our external collaborators, and helped make a number of remarkable advances across scientific disciplines. 🧵👇 4:47 PM · Oct 13, 2026 · 36.5K Views 8 Replies · 41 Reposts · 497 Likes ](https://x.com/pushmeet/status/2110049807385702623) But as cool as these stories sound, they’re just very minor, preliminary efforts. There are a number of reasons to think that AI is going to propel us into a golden age of scientific discovery. That age might not last forever — AI might still run into diminishing returns eventually. But the next few decades are probably going to be an absolutely amazing time for human science. The number of “effective researchers” is no longer limited The most important input into science is scientists themselves. Until this year, that meant human brains — talented, highly trained human brains. The number of trained human brains could be increased by sending more people to grad school, but only to a point — the smartest people tend to be sent into the hardest research fields first, and if you 10x the number of researchers, the average quality will go down. This constraint was already starting to bite, as Ekerdt and Wu argued in 2024: We argue that a substantial portion of the downward historical trend in U.S. research productivity is a composition effect resulting from self-selection in researcher ability and the dramatic expansion of the research sector…[W]e find that this composition effect has lowered average researcher ability by between 37% and 53%[.] And of course, low fertility was making the problem worse, because fewer children meant fewer people who were able to become future scientists. AI just blows up this constraint completely. If an AI agent can function as an autonomous researcher, there’s no constraint on the number of researchers in the world — you can just build more of them, by building more computer chips and data centers that enable the running of more AI agents. Whether or not massive hordes of agents can collaborate to solve individual problems much faster, increasing the number of agents allows them to tackle many different research questions in parallel. What’s more, these AI “researchers” can be trained incredibly quickly compared to human beings. A human scientist takes decades to train, and more years to reach the frontier of their research field by diligent reading and conversation. This is the “burden of knowledge”, and it’s commonly believed to be one reason ideas have been getting harder to find — by the time human scientists can get up to speed, they’re often so old that their ideas are no longer novel. AI just annihilates this constraint — it can assimilate the entire literature and reach the frontier of research extremely quickly, and it can also tell human scientists where the frontier lies without forcing them to discover that for themselves. It can also pivot to new research directions more easily than humans can. And there’s also a self-reinforcing economic cycle here. Because scientific advances enable higher GDP, they enable more resources to be invested into the creation of yet more electronic “scientists”. Now that doesn’t mean the amount of scientific research goes to infinity — there may still be some essential bottleneck task that human researchers are still needed to perform. There are some indications that AI’s progress on open-ended conceptual research tasks is slower than in other dimensions, and it may be that humans are still needed to provide some of this long-term conceptual thinking. I’d bet against this being true for much longer, but it might be true for a long time. Even in this most “pessimistic” case, though, the amount of effective capital that scientists can use to leverage their work just went up by a lot. AI eliminates the theory bottleneck Science proceeds by an iterative process of theory and experiment. The theory part of that cycle is typically short, but non-trivial — it took a couple of years after the blackbody radiation spectrum was observed for Max Planck to come up with the quantum hypothesis to explain it, and decades longer before quantum theory as we now know it was created. It took about a year for Watson and Crick to figure out the shape of DNA after the requisite data was gathered. AI will basically eliminate the theory bottleneck entirely — or at least shorten the turnaround time between experiment and theory by an order of magnitude. AI is an absolute beast at math. Recently, an OpenAI model solved some of the most important open problems in computer science, math, and theoretical physics, and no one even batted an eyebrow because that sort of triumph is becoming increasingly commonplace. One of those solutions was a proof that the Standard Model of Particle Physics could be mathematically well-defined — a problem that human theorists had so far been unable to tackle. Not all research relies equally on theory, but understanding novel experimental results almost always involves some important component of either mathematical or spatial reasoning. That part of the task has now been made much, much faster. And on top of that, science is now open to a greater number of humans who aren’t naturally good at theorizing. AI can read the whole literature Much of the work of human scientists involves reading and understanding endless amounts of papers and going to endless conferences and seminars. The purpose of that work is to understand the existing scientific literature. Understanding the literature allows scientists to do the following things: Come up with new ideas for hypotheses to test Avoid doing research that someone else has already done Synthesize approaches from different areas or fields AI can reduce this effort to almost zero. Consider this excerpt from OpenAI’s blog post about their new research into neurodegenerative disease: The team divided the analysis among thousands of AI agents powered by GPT‑Rosalind, an approach we call distributed biological reasoning. The agents examined a whole-brain CRISPR atlas…a dataset showing how gene disruptions affect different types of cells across the mouse brain. Each agent investigated a gene disruption, analyzed cellular responses, searched scientific literature, and proposed explanations for the results. Their reports were combined into a reasoning atlas linking genes, cellular effects, relevant publications, and possible biological mechanisms. The research team estimated that comparable human analysis and literature review, conducted one perturbation at a time, would take months to a year. The agents reproduced findings from human analyses while uncovering additional leads. Their assessments of novelty and scientific value also aligned with judgments from blinded human experts. These findings help prioritize further experiments needed to test the proposed biological mechanisms. Some of these insights point to new leads for understanding brain disease. [emphasis mine] This is one case in which parallel work by a large number of agents really does make a huge difference: [ Haider.@haider1 this is actually insane openai deployed thousands of GPT-Rosalind agents to analyze millions of mouse brain cells in 32 minutes, work that could take human researchers months to a year also the agents uncovered new leads for brain diseases like Alzheimer's and Parkinson's OpenAI @OpenAI GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration? DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in 2:41 PM · Oct 21, 2026 · 51.5K Views 26 Replies · 95 Reposts · 1.26K Likes ](https://x.com/haider1/status/2112917197903540516) Not only does instantaneous literature review vastly speed up the research process, but it allows AI to make novel conceptual leaps from combining approaches from wildly different fields of research. AI regularly does this in math research, combining results, approaches, and theorems from far-flung places that no human mathematician would have thought to combine. It can do it in science, too. Much of scientific innovation, historically, has been “recombinant” innovation — scientists applying insights from one field to another. With AI, essentially all research is now cross-disciplinary — AI can read not just the literature on a particular subject, but the literature on a million other subjects that might have produced useful techniques or insights for the problem at hand. Superscience So far, all of the factors I’ve listed are ways that AI can speed up science as it’s already done. I’ve basically described how AI can send gigantic hordes of superhumanly fast, superhumanly well-read, superhumanly mathematically gifted researchers to re-accelerate scientific discovery. But it’s also possible that AI will enable types of discovery — and ways of understanding the natural world — that no network of humans, no matter how large or how brilliant it was or how long it had to work, would ever be able to discover. In a post back in July, I wrote about how that might work: What if there are lots of…aspects of the Universe that [are] too complex to understand in terms of simple laws, but not so complex that they just dissolve into unknowable chaos? It’s possible that we can reliably control these complex phenomena with AI, even if we never reduce them to the kind of principles that we can teach a grad student in a textbook… Another way of saying this is that there may be laws of the universe that humans can’t understand but AI can. I call these “cloud laws” — causal regularities that can be exploited by technology, but which are too diffuse and complex for an individual human being to either intuit or communicate… Perhaps social sciences like economics, sociology, and political science obey similarly complex regularities, and AI can help us find them. Perhaps there are physical processes — plasma, or topological materials, or aerial turbulence, etc. — that obey cloud laws instead of chaos?… [H]uman collaboration is bottlenecked — it’s limited by our ability to intuit patterns at an individual level, and to communicate these patterns from one individual to another. AI, being a computer, just doesn’t have this sort of limitation; it can work with vast, diffuse patterns without having to break them into pieces or simplify them in order to compress them into the tiny pipelines of person-to-person explanation… If much of the Universe turns out to be ruled by cloud laws, we could be on the precipice of a scientific renaissance. In order to discover cloud laws, AI will need huge amounts of data about the natural world. AI figured out human speech by assimilating essentially all of the text humans have ever written. If it’s going to figure out hidden patterns in biology, neuroscience, materials science, etc., it’s going to need similarly massive amounts of data from physical instruments. That data will be far, far more than humans — individually or collectively — would be able to parse, label, characterize, or model. But AI was built to do this. If scientists simply build huge arrays of sensors to measure complex phenomena like the brain, the cell, the economy, etc., they may be able to feed this flood of data to AI directly and bypass human cognitive intermediation entirely. AI would then learn about the world directly, and it might learn things that humans never could. This “superscience”, if you want to call it that, would actually go beyond the kind of science humans have done heretofore. Many of the technologies it yielded would be beyond human comprehension — indistinguishable from magic. But to the AIs, they would make perfect scientific sense. In any case, it seems clear that the scientific slowdown that threatened human progress will now be delayed — at least for a while. Unless humans have somehow found most of the actual exploitable regularities in our Universe, we’re about to enter a new golden age of science. Subscribe Share
Quantum achievement unlocked: Scientists capture motion of molecules in action
Advancing X-ray science to ever greater heights in the 21st century is why a dedicated group of Arizona State University scientists continue to push and make a series of new firsts at their one-of-a-kind, compact X-ray science facility, called ASU X-Beams. Having solved their first protein structure, the ASU scientific team have been working to produce ever faster X-ray pulses — an achievement needed to successfully capture the motion of molecules of great biomedical, semiconductor, renew
A look inside St. Francis de Sales High School's brand-new Quantum Science Lab
A look inside St. Francis de Sales High School's brand-new Quantum Science Lab
New Quantum Science Lab being built inside St. Francis de Sales High School
Monterey Data Conference Looks to Future of Computing in Science
Leaders from the national laboratories, academia, and industry gathered in Monterey, California August 24–27 for the 2026 Monterey Data Conference, sharing expertise on HPC, quantum, AI, and the future of computing for science. Oct. 28, 2026 — Initiated in 2019 and now in its sixth year, the Monterey Data Conference brings together researchers from U.S. Department […]
FSU physicist awarded collaborative Department of Energy grant to develop models of quantum matter
A Florida State University condensed-matter physicist is part of a team exploring how quantum materials, which are materials that power everything from
Science Applications Delivers First Practical Quantum Solutions To US Agencies
SAIC delivers the first practical quantum solutions to US federal agencies, bridging the gap between science and real-world applications.
Frank Zhao and Zhengguang Lu Win 2026 LOR Science Prize
Frank Zhao and Zhengguang Lu share the 2026 Lee Osheroff Richardson Science Prize for research on unconventional superconductivity and topological states.
A New Chemistry Breakthrough Could Change How Scientists Build Quantum Computers
By modeling diamond defects as molecules, scientists can predict their behavior faster and identify promising building blocks for quantum computers and sensors. Most materials being considered for quantum technology can hold a stable quantum state only when chilled to extreme temperatures. Diamond-based materials can work at room temperature, but their usefulness depends on defects in
Giving quantum computers a way to pick up where they left off
FIU is researching how to restart quantum workflows after interruptions to preserve computational progress. It lays the groundwork for quantum machine learning for drug discovery and climate modeling.
Quantum Art Research Outlines More Efficient Quantum Approach to Wave Modeling
Insider Brief Quantum Art presented research on a quantum algorithm that uses known wave physics to simplify large-scale wave simulations, with potential
Supercurrent enables the reading of quantum information without destroying it
One of the fundamental challenges of quantum computing is obtaining information from a quantum bit, or qubit. Unlike a conventional bit, which can be read repeatedly without altering its content, direct ...
Scientists overcome a hidden obstacle to more powerful quantum computers
Scientists have discovered a way to operate multiple silicon qubits simultaneously with remarkable accuracy, overcoming a major challenge in scaling up quantum computers. Their new technique maintained accuracy of approximately 99.9% even when all five qubits were running at once, opening the door to larger and more powerful quantum processors.
A tiny defect in a crystal reveals a big quantum surprise
Researchers have solved a long-standing challenge in understanding how tiny defects in materials like diamond and silicon carbide behave when exposed to light. Their new computational method revealed a surprisingly important energy-loss mechanism in silicon carbide that scientists had previously underestimated. The finding helps explain a mystery that has puzzled researchers for years and could improve the design of quantum computers and sensors.
Scientists Just Cracked a Quantum Mystery That's Puzzled Physicists For Decades
The Kondo effect conquered.
Stony Brook Researchers David Miloschewsky and Supartha Podder Resolve a Decades-Old Puzzle About the Limits of Quantum Computing
Finding a path through a maze is hard, even for quantum algorithms. A quantum computer can find the exit of a certain kind of maze far faster than a classical computer. But can it also give you directions? David Miloschewsky and Supartha Podder of Stony Brook University’s Department of Computer Science have proven that finding
OHIO researchers Faiz Rahman, Savas Kaya receive $550,000 NSF award for next-generation nanolithography
Nanoscale and Quantum Phenomena Institute (NQPI) members Dr. Faiz Rahman and Dr. Savas Kaya recently received a new U.S. National Science Foundation award for their research.
Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses
Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
Head of the class: URI quantum physics students work as teaching assistants for Brookhaven National Laboratory
KINGSTON, R.I. – Oct. 20, 2026 – As a sophomore at the University of Rhode Island three years ago, Morgan Beck ’26 began diving deep into the world of quantum physics, wanting to learn more about the branch of science that studies matter and energy at the most fundamental level. At that time, Beck—a 2025 […]
A quantum computing feat brings the ‘megaquops’ era a step closer
Error correction creates qubits that glitch only 5 out of a million times, the first step toward a new phase for quantum computing.
Zapata Quantum Adds Peter Shor and Three Scientists to Advisory Board
Zapata Quantum has appointed Peter Shor and three researchers to its advisory board, adding expertise in quantum algorithms, error correction
Six years of SQMS: asking what limits quantum systems and building the answers
The founding director provides perspective on the first six years of a DOE National Quantum Information Science Research Center — the Superconducting Quantum Materials and Systems Center, led by Fermi National Accelerator Laboratory.
NQPI welcomes Professor Michael Held, strengthening connections between molecular biology and nanoscale materials research
The Nanoscale and Quantum Phenomena Institute (NQPI) at Ohio University welcomes Professor Michael Held as its newest faculty member.
What Kills Schrödinger’s Cat? Scientists Just Ruled Out a 60-Year-Old Theory
Gravity has been proposed as a reason quantum superpositions become difficult to observe in everyday objects. A new experiment challenges one longstanding model of this process. A detector buried deep beneath an Italian mountain has challenged a decades-old theory about why quantum behavior disappears from the world we experience. After 62 days of measurements, researchers
Advance Could Lead to Bigger, More Reliable Quantum Computers, University of Edinburgh Scientists Say
Researchers developed an algorithm that allows standard computers to simulate photonic quantum devices.
Executives from finance, healthcare and energy will explore where quantum could help businesses.
Only approved guests may attend; roundtables will cover combining quantum and AI, security readiness and adoption policies at the Nov. 3 London forum.
Infleqtion Convenes Business Leaders in London to Chart Quantum Commercial Value
OXFORD, England, October 19, 2026--Infleqtion (NYSE: INFQ), a global leader in quantum computing and quantum sensing powered by neutral atom technology, will host the first Quantum Application Forum, an exclusive one-day event convening senior business and technology leaders on 3 November 2026 at IET Savoy Place in London. Executives responsible for technology, innovation and investment decisions will explore how quantum technology can address critical challenges, unlock commercial value and dri
Scientists Solve a Quantum Mystery Hidden Inside Crystals
Tiny flaws in crystals could become building blocks for quantum sensors, and scientists can now better predict how these imperfections lose energy. A laser can excite electrons at a tiny flaw in diamond or silicon carbide, but the energy does not always return as light. As the defect relaxes toward its lowest-energy, or ground, state,
Best of Both Worlds: 'Hybrid' Quantum Computers Could Be Just Around The Corner, Researchers Say
We are so ready for this.
Physicists create a bizarre new quantum state called a fractional fermi sea
Physicists have discovered a strange new state of quantum matter in which ultracold atoms defy familiar organizational rules, forming a highly excited yet surprisingly orderly configuration called a "fractional Fermi sea." The discovery challenges established quantum theories and hints at exotic quasiparticles that scientists have dubbed potential "super-Fermions."
Is International Business Machines (IBM) A Bargain Or Fully Priced?
International Business Machines has delivered a strong multi year share price run. With the stock recently at US$232.14 and cash flows in focus, the live question is whether that price still lines up with what the business can earn and return over time. Over the past 5 years the share price has gained 130.1%, which puts a lot of weight on the idea that IBM’s future cash flows can justify a much higher valuation than in the past. Fresh commitments in areas such as quantum computing and...
Scientists who discovered a third form of magnetism win major physics prize
Physicists have uncovered a hidden third form of magnetism that challenges more than 100 years of established physics. Called altermagnetism, the discovery could transform future computing and electronics while opening an entirely new frontier in quantum research.
Quantum magic state cleanup cuts errors 40-fold in first successful hardware test
Qubits, the quantum equivalent of digital bits, are fragile and prone to errors. That makes building reliable quantum computers extremely difficult. One fix has been to use traditional magic state distillation, ...
China pours record $583 billion in research as US weighs science cuts
Beijing plans to strengthen research in semiconductors, AI, quantum technology and other strategic fields.
Marián Janík Wins ESET Science Award For Quantum Dot Research
Marián Janík wins the ESET Science Award for his quantum dot research with fellowQUTE, helping to advance quantum technologies in Slovakia.
Physicists froze a 'necklace' of atoms — and witnessed a quantum phenomenon predicted 100 years ago
Atoms confined to a single line can bind into clusters called Bethe strings. A new experiment points to a mix of many such clusters, though the evidence is still indirect.
A quantum highway just one atom high could transform superconductors
Scientists discovered that atomic steps just one atom high can guide quantum vortices through superconductors, making them move more than 1,000 times more easily in one direction than another. This remarkable control over quantum motion could lead to a new generation of ultra-efficient superconducting electronics.
Scientists Take Step Towards "Schrödinger’s Cat In Real Life" By Moving A One Centimeter Object Using A Quantum Effect
The diamond is pretty tiny by engagement-ring standards, but from a quantum perspective it is truly gigantic.
QuEra Scientists Named Co-Principal Investigators on Three of Nine Teams in Department of Energy's Quantum Chemistry Program, Now Underway
/PRNewswire/ -- QuEra Computing, the leader in quantum computing, today announced that its scientists will work with three of the nine teams selected for the...
Software advance brings complex quantum simulations to standard computers
A major software advance could aid the creation of bigger, more reliable quantum computers, a study suggests. Researchers have developed an algorithm that enables off-the-shelf computers to run quantum ...
Virginia’s Fairfax County Public Schools builds bridges to quantum computing
The district is installing a quantum computer at one high school that will be usable in early 2027 and has built local partnerships on curriculum.
60 Second Lectures Fall 2026 Roundup Video
This fall’s 60-Second Lectures included talks ranging from quantum materials to U.S. healthcare.
After 40 Years, Physicists Finally Observe The 'Energy Ladder' at Quantum Tipping Points
"To see those predictions borne out is a beautiful thing."
FSU physicists receive funding through Department of Energy's $7.3M Quantum Technology Outposts research initiative
Three Florida State University theoretical physicists are part of a major national initiative exploring how quantum computing could help answer
City of Brownsville Launches Water Security Website
The City of Brownsville has launched www.brownsvilletxwatersecurity.com, giving residents one place to learn about Project Quantum, find answers to common questions and follow developments related to the community’s water future.
‘VisionQuest’ review: ‘WandaVision’ sequel an excellent examination of humanity
Grief, it would seem, is universal, even if you’re an artificial humanoid with a quantum processor for a mind and the face of Paul Bettany. Such is the central premise
Quantum Design Oxford Announces Winner of the 2026 Nicholas Kurti Science Prize
Quantum Design Oxford has awarded Chunyu Guo the 2026 Nicholas Kurti Science Prize, which includes €15,000 and support to attend a physics conference.
Physicists discover “astonishing” property of light that defies expectations
Scientists have uncovered a surprising hidden ability of light that allows it to develop spinning, twisting behavior as it travels through empty space, without special lenses, mirrors, or materials. Researchers discovered that light’s internal geometry can naturally produce left- and right-handed patterns, challenging long-standing assumptions about how light behaves. By carefully arranging a beam’s structure, scientists can control where and how these spinning patterns emerge, unlocking a new way to manipulate light.
Scientists Discover a Strange New Quantum State Where Two Materials Meet
Researchers have directly observed an unusual quantum state emerging at the boundary between an atomically thin material and a metal. Scientists have directly observed an unusual quantum state forming where a material just one atom thick meets a metal. The discovery, made by a research team led by the University of Osaka, demonstrates how interactions
Quantum Meet Quantum: Hybrid Quantum Computer Lets Scientists Peak in on Foundational Quantum Effect
Oxford physicists used a hybrid quantum computer combining qubits and quantum oscillators to observe the Aharonov–Bohm effect.
Scientists stop quantum particle using magnetic field it never crossed
Quantum interference and trapped-ion qubits allowed Oxford physicists to halt particle tunneling using a magnetic field it never touched.
Is International Business Machines (IBM) Stock Worth More Than Its Cash Flow Suggests?
International Business Machines has almost doubled shareholders' money over the past 5 years, yet after a sharp setback this year and fresh headlines around quantum computing and AI, the key issue for you is whether the current share price lines up with the cash the business is expected to generate. With IBM stock now around US$227 a share, the gap between the recent price path and its cash flow story matters more than ever. IBM has delivered a 99.1% total return over 5 years, which puts...
Trump’s $6 Billion Science Plan Targets AI, Moon and Mars
Trump unveils over $6 billion in science initiatives supporting AI, quantum computing, nuclear-powered Moon and Mars projects, and biomedical research.
For The First Time Ever, Quantum Spins Shift a Centimeter-Scale Object
This is (relatively) huge.
For The First Time Ever, Quantum Spins Shift a Centimeter-Scale Object
This is (relatively) huge.
White House Announces $6 Billion Science Initiative Package
The White House says its new science package includes industry computing pledges, a virtual-biology initiative and quantum research competitions, but much of the headline total is not federal cash spending.
SAIC acquires quantum encryption firm
Reston-based federal contractor Science Applications International Corp. has acquired quantum encryption firm Information Security Corp. for an undisclosed amount.
Trump Administration Announces $6 Billion in Science and Technology Initiatives
Washington — President Donald Trump’s administration has announced more than $6 billion in science and technology initiatives aimed at accelerating research in artificial intelligence, quantum computing, nuclear-powered space exploration and …
Nvidia commits $1 billion to U.S. science and quantum computing
The chipmaker announced the commitments Thursday at a Washington event tied to the Trump administration's Genesis Mission program
Three Stanford faculty named American Physical Society Fellows
Wendy Mao, Xiao-Liang Qi, and Alberto Salleo have been honored for work in compressed-matter physics, quantum many-body systems, and organic and polymer electronic materials.
War Department’s $350 Million Quantum Push
The Department of War announced approximately $350 million in quantum computing initiatives, including a conditional $150 million loan to PsiQuantum, alongside broader efforts to modernize American defense research and strengthen domestic technological leadership.
Governor Gianforte Highlights Montana Quantum, Photonics Partnerships at Indian Institute of Science
BENGALURU, India – Continuing Montana’s trade mission to India, Governor Greg Gianforte today visited the Indian Institute of Science (IISc) in Bengaluru to strengthen ties between higher education, Montana’s quantum and photonics ecosystem, and India’s research community.
DOE Announces Science Applications For Quantum Computing
The DoE released eight applications to guide the development and evaluation of fault-tolerant quantum computers for scientific use..
Suspended glass bead entangled with light: Maybe a quantum memory?
Getting a glass bead to act like a quantum object by entangling it with light.
NVIDIA Commits $1 Billion to U.S. Science and Quantum Computing
NVIDIA announces a $1 billion five-year commitment to U.S. scientific research, including quantum computing, energy and healthcare.
DOE Announces Science Applications for Quantum Computing
The U.S. Department of Energy (DOE) today announced the release of Quantum Genesis Priority Applications (QGPAs), a set of eight key scientific utility problems that will guide the Quantum Genesis’s goal of developing fault-tolerant scientifically relevant quantum computers. This effort directly supports the goals outlined in President Trump’s Executive Order Ushering in the Next Frontier of Quantum Innovation and builds on the recommendations made in the Blueprint for DOE Quantum Supercom