Monthly Archives: April 2026

Bonus Info for “Quantum ‘Jamming’ Explores the Truly Fundamental Principles of Nature”

I had a new piece in Quanta Magazine last week, about a hypothetical trick in theories beyond quantum mechanics called jamming.

Sometimes, I get science news stories from contacts. Sometimes I see an academic post something cool on X or Bluesky. But when the stories aren’t coming easy, I open up arXiv.org, click on “new”, and start browsing. And occasionally, I spot something cool.

That happened with jamming. I saw the concept mentioned in an abstract, the idea that someone could “jam” quantum entanglement from afar, like you would jam a radio signal. I hadn’t heard of it before. I wanted to know more. And after I talked to Quanta’s editors, they wanted to know more too.

Jamming is not possible under the rules of quantum mechanics we know. Instead, it’s something that could be possible in a kind of super-quantum mechanics, a theory even weirder than the famously weird theory we use today. In my piece for Quanta, I talked about where the idea of jamming comes from, and why it’s spurring discussion in recent years. In this post, I wanted to give some “bonus info” that didn’t fit into the piece.

One theme I didn’t have as much space to explore is causality.

Quantum mechanics famously seems to do weird things with cause and effect. In a double-slit experiment, photons pass one by one through one of two slits in a wall, headed to a photographic screen. No matter how slowly and carefully you send the photons, their distribution on the other end will show interference between the two possible paths, one through each slit, even though each photon only goes through one. It’s as if before hitting the screen, the photons are simultaneously traveling on every possible path, only to pick one in the moment the photon is detected.

Einstein was bothered by this. He imagined a photographic screen so large it would take light years to cross. How could detecting a photon on one side change the possibility of detecting a photon on the other side? That seemed, to him, to require signals traveling faster than light, which in turn would screw up cause and effect, as any way to send a signal faster than light can also, from another perspective, send a signal back in time.

The answer most physicists accept is that no signal can be sent in this way…at least, in the modern sense. Quantum outcomes are random, so while you could imagine that a measurement in one place changes the outcome in another place, your choice to measure has no effect on that distant outcome. You can’t intentionally send a message faster than light. We call that “no-signaling”, and it prevents the paradoxes of time travel.

Jamming obeys similar rules. A jammer (in the story in my article, a magician named Jim) can modify the entanglement between two distant particles, seemingly faster than light. But he can only do this in a way that involves randomness, so that the probabilities for measurement results for each individual particle stay the same. Instead, he can only modify how measurements between the two particles are related, their correlation. And he can only do this if the two particles can only be compared in a region that he can reach without traveling faster than light.

That’s enough to allow Jim to break the security of many quantum cryptography procedures. He can do this for example by mimicking entanglement: quantum cryptography often uses entanglement to verify that a message hasn’t been tampered with. If you can modify correlations from afar, you can make two particles appear to be entangled when actually they’re related by some other rules, which give you access to the secret that others are trying to hide.

Part of what’s still under discussion, is whether that kind of trick is compatible with causality. This depends a lot on how you think causality is supposed to work, and while the people I talked to are trying to get the story straight, they weren’t in agreement yet. In particular, Vilasini and Colbeck seemed to think that there was an important difference between the way that jamming bends causality and the way that ordinary quantum mechanics does, while Eckstein and Ramanathan weren’t so sure.

More broadly, Vilasini and Colbeck have a broader way of thinking about causality that I only barely touched on. Part of that is ways you can think of one event causing another even if no signal can be sent between them. Part of that is time loops, but of a limited kind: loops that can’t cause paradoxes, because they’re loops of causes, but not intentional signals. Vilasini and Colbeck have argued that jamming, if it existed, could be used to set up these kind of limited time loops, in a piece that was covered by New Scientist. It should be emphasized that these are really very limited time loops, for more reasons than one. They’re also limited to being in only one spatial dimension: that is, everyone in the loop has to be lined up in exactly a straight line. And I got the impression they also require everyone to activate their measurement or jamming devices instantly: with any small delay, the loop breaks.

I said even less about Mirjam Weilenmann’s critique, because there were bigger aspects that the researchers still disagreed on when I spoke with them. Weilenmann’s argument looks at what happens when there are multiple jammers, jamming different pairs of entangled particles. I got the impression from her that she felt she had found a contradiction in these examples, where jamming could only work if it broke its essential no-signaling rules. But Eckstein and Ramanathan seemed to think she was describing a scenario where one jammer could cause noise that would disrupt another jammer, “jamming the jammers” in a sense that didn’t cause any fundamental problems, just introduced jammer vs. jammer combat to make the story more interesting. I opted to not say much about this, since it was clear that things weren’t resolved yet. The researchers are still talking, and I look forward to hearing what they conclude when they reach agreement.

I also didn’t say much about tests in the real world. But that is something Eckstein and collaborators are actively exploring. They’re investigating experiments that could show deviations from quantum mechanics in a variety of contexts, from tabletops in university labs to particle colliders. The hope is that some of these strange ideas could actually be tested.

In general, the impression I got was that despite the seeds of this topic being laid thirty years ago, and reintroduced to the field ten years ago…the topic is heating up right now, in a way it hadn’t before. I’m expecting more jamming papers. If they’re cool enough, I may even cover some of them.

A Window on Absolutely Everything

It’s often said that in quantum physics, everything that can happen will happen.

One way this comes up is in something called a path integral, used to calculate the probabilities of quantum events. If you want to find what happens to a particle traveling from point A to point B, you have to add up a contribution for every path, no matter how windy, that goes between A and B. These contributions mostly cancel out, and matter less the further they are from a straight line, so the straight-line path is, for the most part, a good description of what happens. But in principle, all of the other paths matter too.

The same thing happens in quantum field theory, in more elaborate form. Instead of a path from one place to another, the paths are from one configuration of quantum fields to another, via all the different ways fields can in principle interact. We are almost never able to take account of all these possibilities mathematically, so we have to approximate, organizing the interactions into more and more complicated pictures called Feynman diagrams, each with a smaller and smaller effect.

In principle, these diagrams need to contain every single combination of interactions that might result in the end-state we’re interested in. These combinations can have a Rube Goldberg flavor, with one field activating another, which activates another, only to all cancel out in the end. Because of this, any field that exists, any particle no matter how rare, can matter, if only a little.

And from that, physicists can learn something.

Because absolutely everything matters, physicists get to reason about absolutely everything that exists.

The best example involves something called an anomaly. These aren’t the anomalies of experimental physics, unexpected results that have a tendency to go away with better measurements. Instead of something unexpected, a theorist’s anomaly is something impossible.

Anomalies are combinations of particles that, if they were to show up together in a sum of Feynman diagrams, would break the rules that the theory was made with in the first place. If they show up, they’re a sign of an inconsistent theory, one that doesn’t obey its own rules and thus doesn’t make sense.

In order to have a theory without anomalies, different calculations involving different particles need to cancel. For example, it might be that the charge of different particles has to add up to zero. This means that if you’ve only discovered a few particles, and their charges don’t add up to zero, then you know you’re missing one. There is an extra particle there, which you haven’t observed, that together makes charge add up to zero.

This logic actually works! It was used to predict the top quark. Before the top quark was discovered, the list of quarks, electrons, and neutrinos had electric charges that didn’t add up to zero. One particle was missing, with the same charge as the up quark and charm quark. It was found in 1995, after being proposed almost 20 years earlier.

What AI Physicists Are Missing and What They Aren’t

I’ve seen a couple more thoughtful takes on use of LLMs for physics lately. This blog post by Minas Karamis is particularly nice.

He points out something that I’ve said a version of: an AI that must be supervised like a student isn’t very useful, because the main point of student projects isn’t the paper at the end: it’s training the student. If students don’t struggle through all the mistakes of a project, they won’t get the expertise to one day do greater things.

Someone might object that not all suffering is educational. In the 1700’s, Leonhard Euler calculated digit after digit of transcendental numbers by hand. Nobody asks students to do that anymore, and they still seem to turn out alright. Why would using an LLM for science be worse than using a computer for numerical calculations?

In a word: different skills. Programming numerics teaches you some of the same skills as calculating the numbers by hand: skills at being specific about what you mean, aware of the consequences of the details and their implications. Prompting an AI still requires those skills, to check whether the AI’s output is correct. But it’s much worse at teaching them: unlike programming or calculating, when prompting AI, the consequences of your actions aren’t predictable.

For some, though, there is another objection. Sure, using AI reliably might require those skills now. But when it gets better, surely being careful will stop mattering. Surely the AI will end up doing science on its own, and all that training will be as useful as if we trained the students to play football.

I’m skeptical, but not as strongly as some. I think we’re still living in a time when it makes sense to hire scientists, and train people to think, and invest in your retirement.

I don’t think I have any knock-down arguments for that, though. Just some suggestive ones.

One I’ve talked about before is that a lot of the most important parts of thinking aren’t written down. An AI physicist is going to have a hard time replicating the kinds of methods and approaches that people use behind the scenes, but rarely describe or spell out. It will be easier to suss this out over time, as more data accumulates of people working with LLMs and correcting them. But ultimately there isn’t going to be a lot of documentation of this kind of thing.

Another limitation is memory. A mature scientist can draw from experiences across their entire career. For an LLM, any problem it’s solved in the past is by default lost in each new session. People build structures around this, taking notes and reminding the AI when it “wakes up”, or making documents the AI can be prompted to check. But nothing in this vein so far seems to get nearly as wide-scope or powerful as human memory. A scientist career is still the best way we have to build durable, functional expertise.

Finally, there is a question of costs, and efficiency. Here I’m not an expert, and I get the impression the actual experts disagree. I don’t know whether we should expect scaling to hit a wall, but I wouldn’t be that surprised if it did.

There are other common reasons for skepticism that seem more dubious to me. I don’t think AI is inherently worse at creativity just because they’re trained on existing work, though some of the skills we associate with creativity aren’t very well-documented, and thus are hard to train for. I don’t think AI’s randomness or unreliability is a deal-breaker, because human intuition is also random and unreliable: we solve that with tools, and that’s something AI can in principle do as well. I don’t think humans are “more agentic” or something, except in the sense that most AIs are made by companies who need to make them behave in a customer-friendly way. But an agent is just a game-theoretic construct, a way to figure out can win or lose in situations with defined stakes, and anything you can train or engineer to try to win can be modeled by that construct.

Coming from a place of uncertainty, my main appeal to you is to not get hung up on the bad reasons, either yourself, or from the people you’re arguing with. Focus on the best arguments, and see where they take you.

ArXiv to Leave Cornell

Yes, I’m late to the party on this one.

A few weeks ago, arXiv.org announced that it will be leaving Cornell, the university that currently manages it, and establishing its own nonprofit.

arXiv is a crucial part of the infrastructure for physics, mathematics, computer science, and a few related fields. Researchers post papers to arXiv as what are called “preprints” before the papers are submitted to a journal. In practice, nobody ends up reading the journal versions: the arXiv is free to access, and typically reflects better what the paper’s authors want the paper to look like. So in practice, arXiv is how researchers in these fields communicate, which makes its role enormously important.

If you’re from another field, you might wonder how something like arXiv is financially sustainable. The answer is that it works better than you’d think, but not perfectly. They’ve been supported by philanthropy, in addition to Cornell, and while there have apparently been budget shortfalls and drama behind the scenes, But nonetheless, arXiv has stayed in continuous operation since 1991.

The move to an independent nonprofit is supposed to make it easier for arXiv to get philanthropic funding, which otherwise needed to be filtered through Cornell in ways that were sometimes opaque or didn’t give donors the control they wanted.

While it wasn’t mentioned in the announcements, I suspect another motivation is security. Universities are fixed in place, and that makes them easier to pressure. For an organization that wants to process scientific output in an unbiased way, the link to Cornell represented a vulnerability. It’s not a vulnerability that has mattered yet, and likely didn’t seem like it would ever matter. But it wouldn’t surprise me if they’re more worried now that someone might try to pressure Cornell in order to change how arXiv operates. For critical scientific infrastructure, it’s important to be as independent of those kinds of pressure as possible.