From First Principles - Hypersonic Physics, Deep Sea Life & Princeton’s Millisecond Qubits (EP. 17)

Episode Date: November 22, 2025

Hosted by Lester Nare and Krishna Choudhary, this episode dives into three breakthroughs stretching across aerospace engineering, astrobiology, and quantum computing. We start with a Nature Communications paper from Stevens Institute that experimentally validates a 60-year-old hypothesis underpinning hypersonic flight modeling. Then we head 3,000 meters below the Pacific to explore a newly discovered cold, ultra-alkaline biosphere near the Mariana forearc — a finding that reshapes the search for extraterrestrial life. And we close with Princeton’s millisecond-coherent transmon qubit, a materials science triumph pushing the quantum hardware frontier toward real-world quantum advantage.SummaryHypersonics without supercomputers — Stevens Institute validates the Morkovin hypothesis up to Mach ~6 using krypton-tagging velocimetry, confirming that “simple” turbulence models still work in hypersonic regimes and opening the door to viable, inexpensive hypersonic aircraft design.Life where it shouldn’t exist — University of Bremen researchers uncover evidence of a chemosynthetic biosphere in the cold, pH-12.6 serpentinizing fluids of the Mariana forearc, offering the clearest Earth analog yet for Enceladus- and Europa-like conditions.A millisecond qubit breakthrough — Princeton’s tantalum-on-high-resistance-silicon transmon hits 1.7 ms coherence, 15× the industry norm — drop-in compatible with Google/IBM architectures and a major step toward practical quantum computing.Show NotesHypersonics — Nature Communications (Stevens Institute)Deep Sea Life — Nature Communications Earth & Environment (Univ. of Bremen)Princeton Millisecond Qubit — Nature (Transmon Hardware)

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While supplies last, ends June 30th, terms at AKA.m.m.S. College PC. Goontag internet. This is your captain speaking. Lester Nare, joined as always by my co-host and our resident PhD, Krishna Chowdhury. My friend, how are you today? Doing well. You know, not as well as I could be because it's like raining and cold. It's in Los Angeles. It's miserable. It's a little Seattle right now in L.A. Oh, my gosh. If this is what it's like in the UK, I understand why everyone's miserable. Yeah, yeah. It also reminds me of why I moved out of the East Coast after college and back to my beautiful home of L.A. And I'm not one of those guys who's like, we need this. You know, maybe, maybe like California

needs this. But like in L.A., the water is just going into the ocean. Yes. Like, the stuff that's coming outside of the studio is just going. There's no benefit. Like, the rain needs to happen where the reservoirs are. Yes. To fill the reservoirs. Not here. Yes. Right. We don't need. We don't need. We don't. There is a six to 12 inches of water flood on the road. Yeah, I had to jump over that. I was like... In the driveway? Yeah, yeah, yeah. And there was a, you know, there was a timing battle there because the longer I stood to figure out how to get over that puddle, the wetter I got.

So I'm like, you know, there's a calculation I was making. Like, where am I okay getting wet? I want people to understand, like, houses don't have gutters here. Yeah, yeah, yeah. Like, literally, there's no gutters. So everything just falls off the side of buildings. It's... Yeah.

It's just... It's just a... As I was mentioned earlier, rain has done more chaos and damage than any other culture war issue you want to bring up about Los Angeles. That's true. Rain is our number one enemy. But I think so we have a couple of good stories this week. Yeah. We're on episode 17.

We're about to wrap up season one, which I'm arbitrarily saying is every year is this season. Yeah, that's good. So we're getting close to the end of season one. This is the pilot season. Pilot season. It's kind of like Breaking Bad. it's going to be a cult classic. People thought the first season was slow

and then all of a sudden it was the greatest. Yeah. Yeah, that's going to be us. This week, we're going to touch on three stories. Our first story is about hypersonic aerodynamics. So there's a breakthrough that might enable planes to fly 10 times

faster than the speed of sound. Yeah. We're going to talk about some modeling stuff there. It was in nature communications. out of Stevens Institute, Jersey. Jersey's in two of our stories. Jersey's in two of our stories. I know Jersey gets a lot of flack.

Union County, born and raised, not born and raised. Raised. Raised, yeah. Not born. But no Jersey slander will be tolerated. Story number two, we're going to go deep in the oceans for a deep sea life story. We found some life in these sort of very extreme areas of the deep sea that we did not think was possible. Yes. And there's potentially some implications for,

exoplanet research and the search for astrobiology and search for extra trust real life. Outside of University of Bremen. And our third story is out of our favorite it is our favorite institution. Can you blame us? You can't. It's the best. So like

we just have good taste. Yeah. A Princeton team has built a millisecond trans mom cubit breakthrough which is going to allow us to actually do stuff with the quantum. I'm using the quantum as a term of art. The quantum.

We should trademark that. Better than we have before. So again, this is out of Princeton in nature. Right? So our first story was nature communications. Our second story was nature and earth and environment. But nature nature. Yeah, yeah, now it's nature nature.

The real one, Princeton. This is from first principles. So let's dive into our first story on hypersonics. So for me, as the UAP guy, Mr. UFO guy, I thought you'd like this one actually. The hypersonics is an interesting, so the idea of these things that travel very, very fast. Very fast, yeah. Obviously, the number one arena that this operates in is military because hypersonics as a delivery vehicle for nukes especially is kind of like the thing that is sought after by any sovereign nation that wants global dominance of peace. power.

Yeah. This breakthrough, so the headline on this story is hypersonics breakthrough could enable planes that fly 10 times the speed of sound. This is in nature communications from the Stevens Institute of Technology. But what specifically about this, what is the breakthrough that they're alleging is happening here? Yeah. So, you know, we want to travel faster.

Yes. Right? This is something that'll give us access to faster air travel. It would be really nice if we. We just got to go to, if I could go to India in an hour and visit my grandmother, that would be really awesome, right? Pretty chill. But the age of the Concord is gone and now it's sort of coming back. Supersonic travel is sort of coming back.

Hypersonic travel is something that is on the docket, but it's a little more non-trivial compared to supersonic. Okay. So in order to get into that, there's some fundamental physics involved that I think is very, very cool. Let's talk about the mock number. You know about the mock number, right? It's the ratio of the speed of your object divided by the speed of sound. The speed of sound is about 750 miles per hour. Planes usually operated around like, what, 200, 300-ish? Yeah.

Right? And at the altitude that planes are at, you're a little bit lower. The speed of sound is around 600, 650, right? Because the density is lower. But at the end of the day, the air behaves differently when, you're, you're a little bit more. you are traveling at close to or above the speed of sound. Okay. And one of my favorite movies of all time is the right stuff.

It was a movie about the early U.S. Army tests out in Edwards Air Force Base. And there's a great scene where Chuck Yeager is trying to conquer the demon in the sky. And the demon in the sky is the sound barrier. Okay. Because back then, no one had gone faster than speed of sound. And it seemed non-trivial whether you could. Now, engineers were like, of course you can because a bullet goes faster than the speed of sound. And, you know, Ernst Mock had that famous photograph of the bullet going through the speed of sound that we had covered in some earlier episodes.

So it's obvious that stuff can go faster than the speed of sound and the atmosphere permits it. Okay. It's less obvious whether something as big as an airplane that's carrying a human being can go that fast. There's a scaling issue, a potential scaling issue. There's a potential scaling issue because what ends up happening is when you go faster than the speed towards the speed of sound, the disturbance, the pressure waves, the disturbances that you're creating with your object are going to move away from your object at the speed of sound.

That's what sound is. Sound is the speed at which some disturbance moves through air. But if you're piercing through that barrier, right? then the disturbance is sort of catching up with you. Or you're catching up with that disturbance, right? And you're piling up air in front of you. And air goes from something called incompressible, an incompressible fluid,

which is a way of saying that basically air doesn't change its density as we change its pressure and temperature to something where it does. Now you can squish air, okay? And what ends up happening is at the speed of sound, you get these things called the mock cone, where the wavefront of all of that disturbance, becomes a nice little conical wavefront in your device, like right around the device that's going through.

Yeah, yeah, yeah, yeah. Right. This picture here is of a model airplane in a wind tunnel. Yes. Where the wind is moving faster than the speed of sound, right? So then relative, the, the object is moving fast in the speed of sound and you're getting that really nice mock cone. Right. Right. And the angle of that cone tells you how fast you're going. So like it'll like it'll be wider or more narrow based on how fast you're going. It's kind of like the wake on a boat, right? It's the same principle. It's like the waves in the water are moving at a certain speed, but your boat is piercing through that disturbance faster than the waves can proliferate. Yes.

Right? And what ends up in water, you're not getting this compressibility thing because it's it's just water. It's a two-dimensional sort of surface in three-dimensional. sort of surface in three dimensions so it can dissipate its energy in this like Z direction, right? But in sound, the energy has nowhere to go and so you're just piling up

these air particles one on top of the other, right? So that's the pressure wave that you get when you go supersonic. Okay. We figured that out, obviously. We have the F-16s and all of these like really nice military jets. We have the Concord that used to be a...

The only commercial supersonic jet that has not been replicated at scale since, yeah. Since. Yeah. I think there's recent things coming out of Boeing and NASA that are trying to resurface that and they're trying to make a version of the Concord without the Sonic Boom. Without Sonic Boom problem. Which is going to be very cool. I do know and I, because they're abusing me with advertising on X, there is a

startup that is doing supersonic jet, they're building their own supersonic jet platform outside of the primes. I'm not saying they're accomplishing it, but that's what the advertising is putting forth. I mean, nowadays with like the access to computation and AI and all that stuff, I wouldn't be surprised if a small rag tag team of engineers like cracks, you know, certain problems. Which is fair. So, so that would be very interesting. So, okay, we've gotten to supersonic, right?

Yes. Then there's something called hypersonic. The definition of hypersonic is greater than Mach 5. So five times the speed of sound. Five times the speed of sound. Got it. And there, the air behaves even more differently. Okay?

The physics turns into a different animal altogether. It's kind of like levels in a video game. And like when you get to level two, it's harder than level one. It is harder than level one. And now we're here in level two, right? What ends up happening is the physics of the air, starts mattering. Before, you could just treat it as an ideal gas. Ideal gas, meaning a bunch of point particles that are bumping around. Sure, like even the speed of sound, you can derive

that. Actually, you know, in physics, at Princeton physics, one of the problems on the final for statistical mechanics was to derive the speed of sound from first principles. And I remember being like, thank goodness, because like half an hour before the test, I saw this derivation, and I was like, I'm just going to memorize it. And then it came up

on the test. I was like, dude, I got this. And I just like, and just like rode it down. And then some of my classmates afterwards, like, what the hell was that? And I was like, uh, you know, I just randomly happened upon that page. And I was like, this is something that I should probably just know

how to do. Yeah. In case, and it was the exact problem. Oh, my gosh. I love that. It's a really cool problem where you basically treat the particles like billiard balls, like an ideal gas. And you start asking like how the dissipation, how these billiard balls will bump into each other to create the effective sound.

And then how fast that sound is going to go in terms of temperature, density, and things like that, of the air, right? And the mass of the particles even. Yeah, yeah. There's like, there's, there's terms that go into that derivation. Yep. So, yep. All that is fine before hypersonic.

Right. Okay? When you get to hypersonic, then you start caring about what is the air made out of. Yes. It's not point-like particles. Most of it is nitrogen and oxygen. Okay. And those are diatomic particles.

Okay. Diatomic molecules. Yes. So diatomic molecules means you've got two atoms. Nitrogen is an N2. Oxygen is an O2. Yep. And those guys have other degrees of freedom.

They don't just move. They also vibrate and they also rotate. Okay? So there's other ways in which energy can affect them. And so the ways in which they interact themselves and with each other. And with the object. And with the object. Start mattering.

Because there's more complexity now at when we're starting to get into above Mach 5. Above Mach 5. because the energy scales correspond now. The kinetic energy that you're putting into the air is now at the same regime as the energy of this and the energy of this. You see what I'm saying? Yes, yes.

You see, physics is always about energy scales. It's like what energy scale am I operating in? Right now, the energy in the room is at what, maybe 20 degrees Celsius, right? So 270 degrees Kelvin. At that scale, the vibrational modes aren't. doing much. So you can you can you can you can just use pv equals NRT and be like this is an ideal gas. Yeah. It's fine. It's fine. When you get to higher temperatures, higher kinetic energies,

that's when that's when the molecules start doing this. Yep. Right. And then you got to start caring because there's emergent properties of the particles doing this. Yeah. That matter. Yeah. In the overall equation of what is happening. Exactly. Exactly. At a low, at a low velocity when I'm just like moving my hand around, the molecules are just getting out of the way. Yeah. But if I start moving my hand around at hypersonic speed, the speed at which the energy that I'm imparting on these molecules is now making them go like this. I mean, this is the whole idea of the point that you can't just go infinitely fast because at some point you start your surface of your materials.

Yeah, it'll start melting. Melting and breaking apart. Yeah, like if I go infinite, if I go really, really fast, I'm going to start imparting energy on these materials. Yes. Those materials will start imparting the energy back into the surface. the metal that I have. Yes. Right.

And then maybe that energy is enough to start melting the metallic stuff. The volume of that energy. Exactly. It is getting so high that it is not just, and there's reasons why this makes a lot of sense. But yeah, yeah. I mean, you might have read about this with like, you know, a lot of your UAP research. I mean, it's this idea. It's why, it's why these, all of these, like, advanced aerospace platforms when you talk about, you know,

the M-22, the M-35s, sorry, F-32, F-22 F-25s, like all the B-52s, like all these super-advanced platforms, material science is actually one of the most important aspects of these platforms because we're trying to have them operate both at altitudes and at speeds and with levels of maneuverability that are pushing these limits. Like physical limits. Physical limits of like the stuff that it's in even. And the understanding of hypersonics, the reason why hypersonics is still the realm of massive global power nation states is because it's very complex. Yeah, it's very complex.

All of a sudden, you can't just rely on the Navier-Stokes equation to, like, tell you everything, right? Makes sense? Now it's no longer just viscosity and density and pressure. There's like molecular effects. You got to get down to the molecular level. And that's why it's hard, right? So when it comes to like making something that wants to move at a hypersonic scale, there's two tyrants that you have to deal with.

Okay. There's two types of drag. There's a pressure drag, which is basically my plane is going through the air. There's higher pressure in the front because I'm piling up all the air. Yes. And then I'm creating an effective vacuum behind me. Yes. Right.

So that's going to create a force because there's high pressure here, low pressure here, just like a piston, there's going to be a force that's going to be like, no, I don't want you to move that fast. That's the pressure drag. Yes. Then there's also skin friction drag, which is the air is moving over my body. Yes. Right. And I'm like tearing the air apart in some sense because the part that's stuck to my vehicle, my flight, is going to be moving with me.

But the air away from me is stationary. Right. So there's going to be that like velocity difference. Yes. Right? And then there's another thing, which is the heat. All of the energy that I'm imparting, if I'm going hypersonic, that heat is now changing the chemical structure of the air in front of me.

Yes. And that heat load, that aerothermal load from the viscous dissipation is going to be something that I need to care about. Right. Right. So there's all of these little things. Yes. And the source of most of my problems comes from something called the boundary layer.

Okay. So imagine you've got. got an airfoil, it's like a wing. Yes. What I was saying earlier is there's a thin layer where the velocity shears from zero relative to me. So I'm moving through the air. I'm moving through the air. All of the molecules that are near my, that are stuck to my wing are moving with me. But all of the molecules that are even a few millimeters away from me are gone. Yes. Right? There's got to be some continuous way that I go from zero to my speed.

Yes. Right? Yes. And that's that boundary layer. Okay. That boundary layer turns from laminar to turbulent. Okay. Okay.

When it fully attacks, when it fully attacks the air, it's laminar, meaning it's quite smooth. It's very smooth and it's very nice. Yes. Okay? There's a continuous sort of transformation from zero to whatever my velocity is. Yes. Later on, that laminar flow is going to turn into turbulence.

Yes. And the turbulence is going to be, there's going to be these eddy currents. There's going to be like weird, like circulations happening in the back of my wing. And that transition from laminar to turbulent is a very poorly understood event. I gotcha. Okay. Okay. Yes.

Like seeing where that happens in my wing, how that happens when I change the deflection of my wing. Yes. You know, all of these things are. Or, you know, we've got models for it and we've got ways of understanding it. But at the end of the day, this is an extremely nonlinear phenomenon. Which means that, you know, there's not a simple, nice, like, differential equation that I can just like, do, do, do, do solve. And, like, I have, like, a solution that is, like, in closed form. Okay.

This makes sense. And is it also the case that because of the lack of, to be able to do hypersonics is extremely expensive. Yeah. And it's also classic. Yeah. And so the amount of actual source for data to inform the models is not really accessible. Yeah. In terms of having a feedback loop for how you iterate on your model's predictions.

Yeah. And so you're kind of extrapolating from what you can get, but it's an environment because of this laminar flow to turbulent flow and these new problem sets that happen when you start going hypersonic. Yeah. The ability to have a feedback from real world, real world data. That's kind of a challenge. It's a little bit of a challenge. It is a little bit of a challenge. And, you know, I will, I will go out on a limb and say that even the classified programs

haven't really cracked, like, greater than five mock. Okay. Okay? Yeah. Because, because some of this fundamental physics is just, like, so foreign. Yeah. That, like, there hasn't been fundamental research that's happened to figure that out. Maybe they've cracked it for small things.

Yes. Right? missiles, okay, right? But like you're putting a human in something that's going hypersonic. No, that's actually no. That's actually no. Right.

So that's a difference. You could be going at Mach 10. You could be going at Mach 10 in the upper, upper, upper Earth's atmosphere. Yeah. Right? Yeah. There's not that many particles.

The density of air is low. I mean, okay, is the international space station going at Mach 90 or whatever the hell? Right. Right. It's not really a thing. Yeah, that's fair. Right? That's fair.

But when densities are high, right. It's a different, it's a different. It matters. It's a different like animal altogether, right? And even, I mean, one of the things that I would suggest is the space shuttle, for example, right? The orbitors, they had these over-designed tiles, the heat-absorbing tiles on the very end. They were over-designed because predictive models of that turbulence, of the laminar from turbulent background.

Yes. were so bad that they were just like, okay, I'm just going to make this like 10 times what I need. Right. Right. Because I don't know where that transition is. And I don't know the physics of that. So I'm just going to over design for safety reasons, right? Yep.

So it shows you that, I mean, I'm sure like the top echelons of NASA have access to some of the hypersonic stuff. But I don't know. No, but a key point is, as far as we know at this point in time in the public data realm, hypersonics are exclusively for payloads, like missiles, not for human travel. Exactly. Which is a very different thing. Those are very different things.

Like a human life is worth way more than some nuclear thing that I just like made as I'm testing. Yes. No, no. But that is the key thing. That is a key thing. That is a key thing. And that's where this paper comes in, okay? Okay.

Okay. The central challenge of hypersonic design is predicting and modeling that turbulence. boundary layer, right? And this paper is doing an experimental addressing of that. Of that problem. Okay? And that's why I think it's very exciting. Okay.

What it's doing is basically testing a 60-year-old hypothesis, okay? It's called a Morcovin hypothesis. And what it says is the essential dynamics of this like very high-speed high-mock turbulence is not fundamentally different from low-terminal. from low mock turbulence. Okay. Okay. Morcoven came up with this in 1962.

He was looking at some preliminary data, and he said that this seems to be true. Okay? It's essentially the same. What he's saying is, at high mock, error is going to become compressible, and there's going to be all of these density fluctuations

and temperature fluctuations. But what I can do is I can take the physics that I know from low mock, and I can do a transformation. Basically, I just adjust the parameters for density and I adjust the parameters for turbulence, or sorry, for temperature. And I'm going to get the turbulent effects

that I get at High Mach. Okay? This is a non-trivial statement, okay? Because what it's assuming is the essential physics at the high scale, at the vehicle level, is going to be very similar, right? It's saying all I have to do is adjust some parameters

and I'll get the right physics. There's nothing like weird. That's happening. If it's raining outside, I can just put on a raincoat and I'm fine. Yeah. There are no other derivative thing. Like the addition of a single item that is trivial.

Yeah, yeah. Yeah, yeah. I can just adjust some parameters, right? It's not like rain is like acid rain that's going to melt my rain cold and kill me and all this other stuff. Right. Right. Right. Yeah.

Which is like it is it is filling in and it's not in this it is saying that the this maps in both domains in a way that what we're saying is that is a big deal that we're saying that it's saying that. Yeah, we're saying that there's some there's some trivial transformation that can scale the compress the really complex. Yes. Compressible stuff into just a couple of knobs. Yeah. Into just a couple of knobs. Right. And why that matters is because, like, right now, we have simulations that deal with this kind of stuff, okay?

They're called computational fluid dynamics. They basically churn physics equations in a computer and tell you how the air is going to flow around an object going at a certain speed, given the air density, given the temperature and things like that. Formula One people do it all the time. This is what video games use for, like, water. Yes. You know, like water animations and you're uncharted for or whatever. Yeah, exactly. They need to use models like this. They use models like this.

And like people at aerospace companies use probably more sophisticated models. But at the end of the day, at the end of the day, it's just like it's resting on this hypothesis, right? Because there's two ways to do it. There's a direct numerical simulation called DNS. That's the gold standard. You're basically solving Navier Stokes for like tiny, tiny little packets of space. Okay. And you're getting really granular with it.

Yep. doing a direct numerical simulation for something like an airplane, we don't have the computation. I was going to say, like, we barely have the computation for me to get an image of me making eggs in the morning out of Chachibati, let alone this multi-point. Yeah, this is,

like, imagine every single little part of air that's interacting with every other little part of air, right? How many supercomputers are there that you're going to, like, rent time on? Correct. To, like, deal with every single iteration of your design. it's not going to work. The point is classical computing does not really have the horsepower to do like physics-based

or physics-level models without getting to supercomputer scale. Exactly, yeah, yeah. And there's not that many supercomputers in the world. There's not one like just here. Right, right, right. And then the other, so what we usually do is we use something called a computationally cheap RANDs. It's called Reynolds Average Navier Stokes.

It's basically a way to like blur out a lot of the physics. So on the left, you've got direct numerical simulation. Yes. And on the right, you've got the Reynolds average simulation. You can see that it's kind of a blurred out version of the direct numerical simulation. So it's giving you a good enough answer. Yes. Okay?

Yes. But here's the thing. Every single one of those simulations, especially when you're trying to go into this Mach 5 and beyond level. it's assuming explicitly Markovan's hypothesis, which is that the physics up there is basically equivalent to the physics down here up to a certain skill factor that we can calculate. Okay? The key thing is this. No one's actually like done the work.

I love it. To show that this is equivalent. Yes. Right. That like we can assume these things. We've just sort of been like, yeah. Yeah. Probably.

Probably. Right? We got other things to do. And then came some experimental data that suggested not. Okay. Okay? So there was a conflict. Experimental data came out of something called PIV, which I'm going to go into.

But there on the bottom, you're seeing the red is the experimental data. Yeah, yeah. And the green is the DNS, which is the direct numerical simulation, the really high fidelity thing. Yes. And there's a discrepancy. Yeah. You see that discrepancy? And it is statistically significant.

It is statistically significant. You're going from something like 0.4 to like 1.1 on the y axis, right? That's that's a 2x almost. Yes. So. Okay. Right. Yes.

Now we're in a conundrum. There's a problem. There's a problem, right? If we want to trust this thing, if we want to trust Markovin's hypothesis to beyond Mach 5 in this hypersonic regime, we have to answer the question. One, is Markovin's hypothesis correct? or is the experimental data correct, right? Right.

One of those two is the thing. It's true. It's true, right? Right. So that was the question that these guys at Stevens Institute were trying to handle. Shout out Jersey. Shout out to Jersey because here's what they did. They said, okay, direct numerical simulation is like kind of the gold standard.

Sure. Understandably. How did they get the experimental data? They use something called PIV, which is particle image velocimetry. It's a state-of-the-art optical experiment. And here's what's happening. You got some fluid. You put in some particles.

It can be dust particles, oil particles, aluminum dust. And those dust are going to track where the fluid is moving. Right? Got it. That's the idea. It's kind of like when you put the dye and animal testing to make the things show up when you look at it in certain areas. Yeah, yeah. Because the fluid is.

is just moving around on its own. But you need to be able to visualize it somehow. But you need to visualize it somehow. So you're going to put some tracer thingy that's going to track where the fluid is moving. Okay. And it's going to be seated with these microscopic tracer particles. But here's the problem. Those tracer particles are quite big compared to the air itself.

Okay. Right? The air is made out of nitrogen and oxygen. these are single molecules. The particles are made out of, let's say, 10 to the 5, 10 to the 10, something like that, number of these. So there's going to be a particle lag. There's going to be, the air is going to be pushing this particle,

and then the particle is going to move, and then I'm going to image it and create this velocity field, right? So there's a delta between the thing we're tracking and the thing we actually care about because of this scale difference. Yes. That's not going to matter at low. speeds. But at Mach 5, Mach 6, now all of a sudden, maybe it matters.

That's the question. Does it matter? Does it matter? At high speeds, the fact that my tracer, my dust particle or whatever, is so much larger than the fluid particles themselves, does that matter? Yes. And here's what they did. They said, okay, instead of having tracer particles, I'm going to use something called

Krypton tagging velocimetry. Crypton. They're going to use Krypton, gas. Okay. And the Krypton atom, which is a monoatomic gas, because it's a noble gas,

it doesn't make molecules, that's going to be my tracer. Because it is closer in size to the thing we are trying to trace. Track, yeah. Than the other aluminum dust and other things. Exactly. And so we can,

the point is, if we do it this way, we can start to rule out the lag problem as the source of the issue. Because the krypton is just going to be going with it. Yes. It's going to be moving at the same level.

and paste and all these things as the actual air. And this is the part that was like a little bit non-trivial to me, right? It's like with dust, it's really easy to set up a 2D laser field and like image the dust. Because the laser, remember, from first principles, how do we image something? We got to capture light. Yes. So what do we do? For the dust, for the particle image velocimetry, we set up a 2D laser field.

the dust is moving around the laser bounces the light from the laser bounces off the particle and then it goes to our detector and we take an image with a single krypton atom how are you going to do that here's what they did and it's

I thought this was very cool so they got a laser and they used the laser to ionize krypton okay they have this two photon procedure where they focus all the laser beam into a tiny

spot where the krypton is getting seeded. Okay? And if two photons interact with a single krypton atom, it's going to bump up in energy level and ionize itself. And then you're going to be able to track it. And now this krypton is an ionized thing. And when it goes back to its normal state, it's going to release a photon. The krypton itself is going to create the photon that we're going to use to track it with. So there's like a read. So there's a write

mechanism, right? We're writing the Krypton, we're tagging it. And then we're reading it later when the photons released. Yeah. And we're basically created read-write level permissions. Yeah, on like an atom. Like on an atom.

And we're tracking it as it goes through this mock, mock five, mock six. I hope people who are watching and listening to this understand how outrageous what you just said is. That's so, like, and it took him 10 years to like figure this out. Obviously. Like, like,

credit to them, dude. Like, that's so, that's such a cool idea. That is a really, that's actually really, right? Because you got to think like, that's really, that's clever. Yeah, it is clever because I want to trace how the gas is moving around, but I want to image the gas itself. Right. Right. How do I image the gas?

I can't image atoms. Right. Right. I can image dust particles because they'll scatter light. Yes. But if I make the atoms themselves like this weird light bulb. Yes. This reminds me of the story we talked about in episode 16 last week about how we were used the camouflage story about how we're manipulating biology in order to generate an outcome based on its existing evolutionary processes and building into that infrastructure in order to get the outcome we're looking for.

Just conceptually, it's like using the fundamentals of physics. It's like we're eliciting the reaction that then enables us to do the thing we want to do based on the fundamental principles. We're not just like, we can't just force the answer. We kind of have to like work with the universe a little bit. Yeah, yeah, yeah, yeah. And so and so and Krypton was the way to do it. And Krypton was the way to do it. I think that was really cool.

That's really cool. And so what they did was in the experiment, they have something called the Stevens Shock Tunnel, which is at Stevens University. It was funded by the Office of Naval Research. it's an impulse facility. And what it does is it creates these shock waves that move through that tunnel at like Mach 5, Mach 6. Right. Right.

So you've got some apparatus that like where the Krypton moves through the tunnel at Mach 5, Mach 6. You can now measure, you can ionize the krypton in one part. You can track it. And then you can get that level of fidelity that was never possible with the older method. Yes. Right. And the, the result.

speak from themselves. You get the mean velocity curve and the Krypton tagging KTV data perfectly aligns with the DNS. Right. As opposed to what we looked at earlier, where there was that delta, that discrepancy, meaning we're now actually tracking in a modeling context close to the direct actual, like the direct value we see in that really high fidelity gold standard. Yeah. We're now able to kind of replicate. that with like less. Yeah, yeah. And now it's in the real world and we can say, okay, experimentally, the model is actually tracking. The model, okay, that's, that's, you know. Yes. It's like, it's like the particle stuff that we were doing earlier with the dust particles,

that wasn't good enough at these, at these velocity regimes. Above Mach 5, we now have a regime, a velocity regime that can work with lower, without needing a supercomputer. Exactly. We can, we can, we can rest easy that the simulation framework that we've been using is valid. Is lore accurate. Yeah. It's like it's, it's definitely less fidelity. Yeah. But we don't have to, we don't need a supercomputer to, let's say, test a bunch of designs before we like go into production.

This hugely reduces the scale of engineering, right? That makes, yeah. Because I can now try a bunch of different things on my computer. It can be a big computer, let's say, but it doesn't have to be a supercomputer. It could be an M5 Mac. Yeah, yeah, exactly. Like it can, Markovin's hypothesis is now experimentally validated at Mach 6. At Mach 6.

At Mach 6. Right? Yes, got it. And that's kind of like the key point is like this 19 was a 62? 62, yeah. sort of hypothesis has now been validated a scale of high fidelity measurement above Mach 5. Yeah. And we hadn't gotten that far before, experimentally. Experimentally, we hadn't really known is the hypersonics regime, as opposed to the supersonic regime, have something where Markovins hypothesis is no longer valid above this limit.

Yeah. And we've now, no, no. No, we're good. We're good. We're good. So now it can, you know, we can start, like actually pushing these measurements. Yes. Right?

To greater fidelity. And we can also start, engineers can be a bit more confident about the designs that they produce in silica in the computer. Yes. And they try to validate it. Yes. Right. You can now be a bit more confident about, okay, this thing works in the computer.

Let's try to actually make it. Right. This is, this is, I'm looking forward to it, dude, hypersonic travel before I'm, before I'm out of here. That'd be great. That would be dope. That would be fantastic. That would be fantastic. I mean, it's probably going to be like $10,000 a flight, but as we know, they always got to monetize. Yeah. I, I, I will, before we move on to our story number two, I just want, it's funny, the timing of this because literally earlier today I saw, or I think it was today. Yeah. Sam Altman made this post about. Anyway, long story short, there's a company that has reached criticality on a nuclear reactor infrastructure that is venture capital funded. It is the first BC funded entity that has been able to show the ability to reach the splitting of the atom in a new reactor architecture. And they did it in computer.

Oh, okay. And then this was the first non-heat, no heat, no power, but like, I guess physical test of that simulation. And they were able to theoretically reach that point. It is just another, all these real-world examples of simulations moving to real world. And like, I don't think like the everyday person understands how much we depend on modeling. Oh, yeah. And simulations. It's insane.

To be able to, like, validate ideas before we even start putting, No, because that stuff is expensive. It's so expensive. Making stuff in the real world is super expensive. So whenever people are building new chips, the first thing you do is validate it in silica, right? You have CAD designs and you have all of the software. Whenever Nvidia makes its next H-4,000 or whatever the hell, right?

It's going to create so much testing within the computer because we just know physics at this level so well, right? We're not going into CERN trying to find the H-E-R-R-R-R-R-R. to find the Higgs boson. We're like, we need to, I don't know what the mass is. I know what the mass of the electron is. I know what temperature. Like, you know.

Right. Like, so, so we know physics so well that it's just way cheaper to do stuff in silica, in a computer before we rig up a whole factory to actually make stuff. Yeah, you're right. Which makes total sense. And it's an important. I got to check this out. That's pretty cool.

No, it was, it was, it kind of took off on X earlier today. And, you know, I'm not, I did try to do some very brief validation. It does appear to be the first time that a private venture back, like investor back, like private capital has been able to do so, which is, that's something. And it was, I always look at the comments on X. It was part of a DOE program to get three private versions of reaching criticality by 26. So it's a part of a sort of an ongoing DOE program. Yeah. Which made it a little bit more. Yeah. Yeah. Definitely a little bit more legit. I mean,

this was funded by the Office of Naval Research. Same, same idea. A lot of federal government funding. It's all another reason why we need more federal funding. All the beautiful things we love. You know, it requires. Yeah. Yeah. We're going to move on to our second story, which is about microbes. And a new biosphere where we thought life was impossible on Earth. And it has some implications

potentially for astrobiology. So the headline on the story, Life found in place scientists thought impossible. Yep. This is a paper out of nature communications, earth, and environment. A biomarker evidence for our serpentinized. chemosynthetic biosphere

at the Mariana 4 arc. This is out of the University of Bremen, German University. Many people are familiar with the Mariana Trench. People sort of through a variety of sorts of minds, oh yeah, like there's like these hot vents and there's these things that live in these hot vents and that's the most extreme place.

But there is something really interesting here about life defying the limits that exist in the deep sea in this paper. Yes. Yeah, I think it's very cool. The core concept of life is constantly like evolving in places that we never thought was possible. This one is happening near the Marianas Trench. And it's very different from the deep sea thermal vents that you were just looting to actually. Okay. Perfect. Because the deep sea thermal vents usually happen at places where the oceanic crust is diverging. Like in the mid-Atlantic ridge. where there's a there's the crusts are like splitting apart so there's a lot of volcanic activity happening right right there yep um and so there's a lot of like sulfur gas and all sorts of stuff coming out and then all of the bacteria are are capitalizing on that this one is a subduction

zone okay the marionis trench is something where where one plate is going under another rather than two plates splitting, two plates splitting, right? In the Mid-Atlantic Ridge, they're splitting. Here, one is going under another, right? So it's a little bit different, and I think that's what's really cool. So life is constantly trying to redefine boundaries, right? We've got extremophiles everywhere. Yellowstone, the life in the hot springs, that's something that's in every single biology textbook.

You've got hyper-salin ponds in the Atacama Desert that somehow managed to have. bacteria. You've even got like nuclear reactor cores of power plants. They've got weird bacteria. And I want to cover that in another future episode because those things are very cool. This new paper, it's a landmark 2025 paper and it provides biomarker evidence for serpentine chemosynthetic biosphere. Okay. In the Mariana's trench. So the setting is something called a Mariana four arc. right under Japan east of the Philippines is the Marianas Trench

and you see that like the sort of crescent of the Marianas Trench The Captain Hook? Yeah, that hook is the Mariana's Trench right? That is one plate subducting

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Book on Hilton.com or the Hilton app and save up to 20% to get the stay you expected. When you want savings, not surprises. It matters where you stay. Hilton for the stay. Under another, okay? And right, not in the trench itself. The trench is like the bottom of that subduction zone, right?

Where the ocean is like, a few Mount Everest deep or whatever. It's very, very deep. Here, this is a part of the of the subduction zone that's at the top. Okay? It's only about 3,000 meters below the surface, but it's still impossible conditions, okay? Hyperalcline pH. So you've got 12.6 pH.

Jesus Christ. Okay. Water is at 7, and we're going to get into that little bit. Okay. It's really cold, 3.5 degrees Celsius. right? So that's about maybe what? 40, 37 degrees Fahrenheit. Very cold. Very cold still. I mean, I might have surfed in that kind of temperature, but I didn't have a good time. Yeah, exactly. But somehow these bacteria are living there. And there's also a nutrient scarcity.

There's profound limitation, especially when it comes to phosphate. Right? There's not a lot of phosphate there. And what's really cool is this paper finds life there and then tries to characterize how that life, is doing living down there, which I thought was very, very cool. Yes. The geochemical engine is something called serpentinization. Okay. It's a reaction between water and whatever mantle rocks are down there, because this is very close to the Earth's crust, right?

So you've got water, you've got some rocks, and what is output is a mineral called serpentine. you've got a lot of molecular hydrogen. Okay. H2. And H2 is a very energetic molecule, right? You can burn H2 and create energy and water and CO2. So that's what's very interesting here.

Okay. And the analogy is like, you know, when you've got iron and minerals, that iron splits and reacts with the oxygen and creates hydrogen. because the water, it's going to strip the oxygen out of the water and create H2. Something similar is happening down there, right? The minerals are reacting with the oxygen in the water and liberating the hydrogen. Yep. Okay.

And then that hydrogen then goes and then creates methane because there's going to be carbon there too. Because there's a free, it's free hydrogen floating around. Yeah, free hydrogen floating around. And that fuel is then going to create abiotic methane. So this is not methane coming from life. This is methane just coming from the chemistry down there. Got it. Okay.

You know what I mean? Yeah. No, that makes sense. Yes, that makes sense. And so that's what's really interesting. We got to understand sort of the tectonic setting here in the Marianas Trench. So I told you, right, it's a subduction zone. Yes.

You've got one plate going under and another plate going over. And the fluids in that subduction zone, in the mantle itself, are what create the serpentinization. You've got like a sort of chemical process that like rejuvenates the ocean floor with new chemicals and things like that. But that resulting chemical is like, the water there is like really, really messed up, okay?

Which makes sense. Which makes sense. It's like way deep. It's near a subduction zone. But the pH is 12.6. Okay? That's like bleach and oven cleaner. Yeah.

It might cure COVID. I heard that. Oh, yeah, yeah, yeah. So we're just going to go to the Marianas Trench. Right. Get it. Get it. And then drink it.

Yeah, yeah, yeah. Got it. Yeah. But somehow, so these bacteria don't have COVID for sure because they live there in that bleach and oven cleaner of 12.6. And just to tell you just how bad that is, right? So the pH of water is seven. Water is at a pH of seven.

And what that means is pH means percent hydrogen ion. Okay. It's a log scale, meaning that, Water has a concentration of H-plus, which is 10 to the minus 7 moles per liter. Moles is a unit of the amount of stuff. It has to do with Avagadro's number. It's about 10 to the 23 thingies. Like a dozen is 12.

A mole is 10 to the 23 times 6. So I'm not going to order a mole of cupcakes from the bakery. No, no. But you would order a mole of water. Right? Actually, no. A mole of water is like, I think, 55 liters. So maybe not.

But it's something like that. A mole of carbon 12 would be 12 grams. Got it. Right. So that means that there's about 10 to the 15 hydrogen ions per liter in just normal water that we drink. Okay. Something with a pH of 12.6 means that there's 10,000 fewer hydrogen ions and 10,000 more OH minus ions. Because the oxygen, the H2O can split into an H plus and an oh H minus.

Yep. Right? 10,000 more OH minus ions and 10,000 fewer H plus ions. That is an incredibly basic environment. There's all these OHH molecules with a minus, right? They're an ion. It's extremely reactive. And the fact that anything can just like stay stable down there is incredible.

Got it. Okay. Yeah. Right. What we're trying to identify is the base environment in which we are trying to even operate is not ridiculous. is not conducive to operation. Yeah, yeah, yeah. It's ridiculous.

To like stuff that we know. Stuff that you're right. Right, right, right, right. So here's how they can, uh, they, um, they got the sample. They have something called a gravity corer. Okay. It's a, it's a, it's a core. So what they do is they, they, they get like a 1.4 ton weighted steel pipe.

And they just shoot that down into the, into the ocean floor. Yes. And they grab like two meters where. of soils in there, right? They bring it back up into the surface. Then they take the mud out. The mud has this blue hue, which is serpentine, blue serpentine. Okay.

There you can see. And you can do mineralogy on it. You can do x-ray diffraction and thermogravity, and you can identify minerals like serpentine and bruise and things like that. The problem, and what you want to do is you want to find life there, right? The immediate thing that anyone would do, it's like, okay, I want to find life. What do I do? I look for DNA.

Yeah. Easy. Yeah. I look for DNA. This thing has about 10 cells per cubic centimeter. That's really low. Right. Okay.

Usually you got something like 10 to the 5 cells per cubic centimeter in soil. Okay. Okay. Soil has a ridiculous amount of cells, a ridiculous amount of DNA. This thing has only 10. So what's going to end up happening, if you try doing something like PCA, which is polymerase chain reaction. It's basically a way to amplify DNA in your sample. So whatever little bit of DNA you have, you multiply it and then you try to put it through your measurement

sticks and you try to figure out what the thing is. If you try to do that, you only got 10 living cells. Most of the DNA that you're going to have is just going to be dead stuff that's conglomerated on the ocean floor. Right. Because the ocean has just raining dead. Yeah, dead material. All the time. All the time. Phytoplankton. Maybe a whale died somewhere.

So you're going to have all of this random DNA and not the actual DNA of the stuff living in there. Not a perfect analogy, but if you try to take a photo that is really low resolution and blow it up to really high resolution, you're trying to take pixels that didn't exist and make them larger. And it's just going to be a larger version of not a lot of information. Yeah, exactly. Like if you did one of those like AI depixelizers of a really bad photo, it's just going to start making up stuff. Because you don't have actual. This is dead information from potentially a lot of dead stuff.

A lot of dead stuff that's just been raining down on the ocean floor. Yep. Right? So you can't really do that. Okay. That makes sense. So instead what you do is you go for trying to detect lipids, which are fat molecules. Okay.

Lipids are the stuff that make up the cell membranes. of your cell. Every single thing has this. Okay, a lipid membrane. You need a lipid membrane because how do you define life? Well, you need an inside and outside. It's the border. How do you define a country? You need a border.

Yeah, exactly. Yeah. But how do you define life? You need a border. You need a 2D border for a 3D object, right? And that is always a lipid, a bilipid membrane, right? So every single thing of life is going to have these lipids. Now, there's to be two types of lipids. There's going to be the living

lipid from whatever's living in there. And then there's going to be the dead stuff that's raining down. How do we actually discern between the two, right? And so here we're going to have two different types of lipids. There's the core lipid, which is lipids that look like this.

They've got a part that likes water on the top, the purple one, and then a part that doesn't like water. And what you do is you make two membranes out of this. So there's a part that likes water that is facing the outside, that is facing both the inside and the outside. And then the inner part is the part that doesn't like water. So now you've got a way to separate water from water, right? So you've got a way to separate the cytoplasm of your cell,

the stuff that's inside from the stuff that's outside based on a wall that the water really doesn't like to cross. Yes. Those lipids have two parts. There's the part that likes water, the hydrophilic part, and then the hydrophobic part, which is the part that doesn't like water. If you've seen those intact, that means it's pretty recent. On the other hand, if it's died for a while, those two will have clipped off. Yeah. And you're not going to see the two together. Together.

Okay. Right? So you've got these intact polar lipids. Okay. Right? And that's what these guys were looking for. That makes sense. The point being by finding these, what do you say, bipolar lipids?

Yeah, polar lipids. both sections still being in close proximity, meaning that they were preserving something inside that was like a living process. That means it was as close to living as we can get because they're still in close proximity. As an organism dies, those things naturally because they no longer need to maintain the life inside,

they will part away from each other. Yes, exactly. And so this is like a tangible way for us to be like, this is recently living or currently living versus this is for sure dead. Exactly. Exactly. Right? So that's one way of doing this.

The other way to do this is to actually look at the carbon isotopes in your life. Right? There's two stable isotopes. There's carbon 13 and carbon 12. Carbon 12 is way more abundant than carbon 13, but both are sort of mixed in together. there's a baseline like there's a baseline abundance of them but life is going to have

way more carbon 12 than carbon 13 because it's lighter and so whatever enzymatic action is happening the enzymes are lazy and they're going to use the carbon 12 more than the carbon 13 because the carbon 13 is harder to move around right takes more energy it takes more energy right and so for whatever little bit of energy you have you're going to usually just by probability fix the

carbon 12 into your organic chemistry more than your carbon 12 carbon 13 so by looking at this abundance we can also tell whether what we're looking at is alive now or has died and it's just sort of you know part of the thing yes right yes and from this they they found that there's there's one thing that was really cool is when they look at the mudline right they have this two meters two or three meters of the ocean floor yes at the very top that's close to the ocean floor they don't see a lot of these biomarkers. So a lot of the stuff is dead. But underneath, they're seeing living organisms.

Right? So there's a living tissue that's happening underneath. There's a layer of dead stuff. Yeah. And then there's active biology. Underneath it. Underneath it. Right.

And by doing a bunch of different oxidation measurements and like HPLCs and all this other kind of stuff, they came up with a really complicated ecosystem. where you can actually find, like, what were the things that are making this happen? And they have an hypothesis where the methane comes from the bottom and the CO2 comes from the top. And the water comes from the top. And they have all of these different mechanisms that they've actually traced through to create a magnificent ecosystem. Right? There's a circle of life just for this.

Just in there. Just in the same area. Isolated from the sun. There's no sun. Right, right, which is important. This is important. It's isolated from the sun. It's completely driven by chemical processes that are happening because of the interaction

between water and the earth's crust, right? And it's completely in isolation from the rest of life on Earth. I think that's a foundational claim. Because, and again, I think just to kind of part of it, the implications a little bit here. Part of the point is, for a layman like myself, a lot of times we'll think of

sunlight, solar energy as being a required prerequisite for all life. And when the sun goes away, you can't have the light life because that's the only source. This is why a photosynthesis

is so foundational and all this other stuff. What this is saying is, independent of sunlight. And this goes back to your point earlier, but why it's different than the heat vents. Yeah. Or heat.

Or heat or the sulfur, the active stuff moving out. This is just like slow chemistry happening. In the crust. And out of that slow chemistry in the crust, you still get life. You still get life. They still find a way, right?

As Jeff Goldblum was saying, like life finds a way. no matter where. And it's incredible how they find a way, right? There's all these adaptations that they've come up with. For example, in there, in that high pH, those lipid molecules are just going to become like soap. Right?

So how do you prevent the soapification of my cell membrane? Well, instead of the ester bonds, they're using ether bonds. So instead of a carbonyl bond where the carbon is attached to an oxygen and then two groups, you've got an oxygen that's mediating that. And that oxygen becomes the anchor for your phospholipids. Not phospholipids, sorry, glycolipids because you don't have a lot of phosphate. Right, right, right, right. You're using actually like sugar in a bunch of lipids to actually create your cell membrane.

The other thing that's kind of crazy is that, you know, at that temperature, fat freezes, right? Butter is a solid in the fridge. But that's because it's a saturated lipid. If you use unsaturated lipids like oil, that's not going to freeze. And the reason why it's unsaturated is because you've got these kinks in your carbon skeleton. You've got a double bond instead of all single bonds. And a double bond means you're going to have a kink in your carbon skeleton, which means it's not going to stack up as nicely as saturated lipids well. And if you're saturated, then everything's going to stack up and you're going to become solid.

But without the saturation, then you're going to remain liquid and you can have that malleability. you need for life. Yeah, yeah. That's, and it's so funny that it's literally that double bond kink of, let me actually pull that back up. That was photo 12. That double bond kink, yeah. Carbon in the middle.

Yeah. Literally just that. Just that. Totally changed the entirety of the, the expanded structure that you scale up to. It's kind of crazy, right? That's crazy. Like life figures out ways to make it work. Yeah.

You know? Using fundamental, it's just fundamental physics. The double bond creates a kind of weird quantum mechanical effect here where like now my bond can't be linear. Right. And which means at this temperature, it can't just then solidify into a block. It has to remain in that. Look at that. It's so cool.

Yeah, I like that. It's so cool that like life can find this out. And this has huge astrobiological implications. Obviously. And for me, that's the coolest part. The cool biosphere analog, right? Like the Mariana's Trench is this first high fidelity analog of cool serpentinization in the cold. In the cold.

Okay. Not in the hot. Right. Not in these zones where the plates are drifting apart. And then the heat's coming out and sulfur and all these. This is cold. That's actually really important. So like all of the ice moons become much more interesting.

Yes. Because of the existence of proving that cold serpents is cool serpentization. can have a biosphere. Exactly, exactly. And Enceladus is a big one. Cassini actually sampled plumes. This is a moon of Saturn. And Enceladus is something where we know there's an ocean ocean.

Ocean. There's an ocean there. There's a crust on top. And there's so much geologic activity that there's actually geysers that shoot out plumes of stuff into the cosmos. And Cassini, we were lucky enough, the Cassini got close enough. that it actually sampled those plumes. As it was flying by.

As it was flying by. And what we measured was hydrogen gas and methane. Which is exactly what we have here. Meaning. And so like just, I know I'm extrapolating. Yeah. This is an extrapolation. However, given what we just walked through about what we've seen at these two plates. Yeah.

in the Marion's Trench on Earth where we were able to send down our little torpedo pull up meters worth the top layer was dead but below that the same chemistry that exists in that region here on Earth mimics the chemistry that Cassini

pulled up. Yeah. And there's a direct match. There's a direct match. That's not to say that there is life on Enceladus. However, the chemistry... The chemistry is very same, same. It's very similar. Yeah.

A pizza in New York is not the same thing as a pizza in California. Yeah. I get it. Yeah. But it's still a pizza. But there's possibility there. Yeah, which is so exciting.

That was just so exciting. That's so exciting. The other thing with Europa, right, models predict that serpentinization is actually more probable than let's say deep sea vents on Europa, which makes sense. And now we've got Europa Clipper going there. It's going to do a bunch of things. If we're going to send, you know, if we get our funding back and JPL gets its funding back.

We need our funding back. Yeah. But if we do, then we're going to send, you know, let's say something that drills into the Europa ocean and then tries to look for it. Well, those things should probably have a mass spectrometer, right? Because the mass spectrometer is what was used to actually characterize these lines. lipids and these lipid biomarkers. So maybe let's not look for DNA. Maybe let's look for lipids. This introduces a whole new paradigm for what to even look for when we go to these distant worlds looking for extraterrestrial life. That's a brilliant insight in that what we look for

determines the results. And it's the pushback that people have given about SETI looking for radio signals. And it's like any sufficient technological da-da-da, like would they be still using it, yada, yada. I mean, in an analog, in an analogy, what we're saying is historically, maybe we'd always be looking for DNA as the sign of life. Looking for lipids, the presence of lipids, is another signature. Yes. That is equally relevant and viable in that search and might be more fruitful because even on earth, we can see in regions that we previously believed to be uninhabitable, life is there, but the way we found it was not through the DNA, was through the discovery, the lipid presence. Yeah, exactly. And like, who knows, maybe they have a different

form of genetic material, right? DNA is like this arbitrary thing that perhaps our ancestor four billion years ago came up with, right, to encode data. Right. But there's, there's got to be other ways, right? It's just whoever came up with it first was like, this is dope. Right. And then just, like, ran with it. Right. Yeah. Right. Right. We are living with the choices that the internet pioneers of the early 90s decided how a browser was going to work and how graphical user interface is going to work. And that's just what it is. And now we have to deal with that. That is a very interesting story.

That was very cool. With huge implications, not only for, again, expanding our idea of what does life mean just here on Earth, but then informing how we ask questions about the universe around us in that same journey. Uh, fascinating, fascinating paper. Um, we have that one again from University of Bremen. That was in nature, communications, earth and environment. This, this week of stories is fantastic. We're going to move on to our third story, our last story of the day, our Princeton story. Yes.

We've, we've, we've kept a couple weeks of not shilling Princeton. But like, when you're the best, things happen. I don't know what to tell you. Like, it's just, it's a breakthrough. And so we cover breakthrough. The millisecond transmon cubit breakthrough out of Princeton. This was in nature, no subjournal. Yeah.

Nature mainline. Princeton's new quantum chip makes a major step toward quantum advantage. Now, in a previous episode, you explained to me the distinction between quantum advantage and quantum supremacy. Yes. And the context of Google's paper claiming that they've achieved quantum supremacy, which received a significant amount of, pushback. But this is about Princeton's quantum computing for a new qubit, the cubit being like the fundamental unit bit in a classical computer. Exactly. Cupid and a quantum computer. So what is it that we, literally we as in you and I personally? Yeah, yeah, yeah. Hey, I donate. I donate.

I donate. Money goes to the last month being like, hey, are you going to the class of 2014. I was like, yeah, fine, fine, fine, fine, fine, fine. As long as I get to claim that I was the one who came up because quantum breakthrough. So talk to help me understand what, because we talk about quantum quite a bit actually. We do. So this is, it has to be actually a big deal. Yes. It is a big deal.

Yeah. So quantum computers, right, the promises to break encryption, simulate materials, you get a better portfolio for your financial stocks because it's going to like figure out the best way to do things. And all of that relies on actually making a quantum computer. right? And making a quantum computer as of now, it's still a hardware problem. Okay. Okay. It is still a hardware problem. A quantum computer, let me just go through briefly what a quantum computer is. Okay, it uses a qubit instead of a classical bit. So a classical bit is your transistor that is in a state of either I'm going to let current through or I'm going to block current. So that's my zero and one. A qubit can be in a superposition of two states. It can be in, a zero and a one, and it can be in a linear combination of something in between. The other thing that's different about a qubit versus a classical bit is a classical bit,

one transistor doesn't really talk to another until it's like really told to, right? It's like maybe one transistor flips its sign based on the state of another transistor, and that's an explicit sort of thing that we're doing within the algorithm of whatever we're implementing. But in a qubit, the power of the cubits is that they can be entangled to one another. They are not independent, right? So one cubit can be in a superposition of zeros and ones. Another cubit right next to it can be in a superposition of zeros and ones and so on and so forth. And I can get access to an exponentially large number of states in my memory without actually having to explicitly encode each one of them.

Right. As a terrible analogy, because I'm always trying to figure out ways to think about it in my head. If you have a relay race, a four by 400, classical computers, you have to wait to hand the baton to the next runner. Yeah. But in a quantum computer, my first lap, the first runner, can have, like, multiple runs. And like, whichever one happens to get there first is like, and so it's like you're... All four just basically cheat. Right, right, right.

Go through the whole thing. Yeah. But just trying to like contextualize like what the difference is when you say that a qubit can be in a superposition of both zero and one, there's not, you're not waiting for the handoff in order for the next thing to do its next process.

Yeah, yeah. You can do a lot of things in parallel. In parallel. And you can access a lot of computational stuff in parallel. Yeah, yeah. It's a weak analogy, but it'll work. Okay, actually. And at the end of the day,

how a quantum algorithm works is the following. There's something there's very famous called the Shores algorithm. Okay, this is the one that all the NSA and everyone is excited about, because this is the algorithm that lets you break RSA encryption. Okay? But

it works like any other quantum algorithm and that here's what happens. You've got a bunch of cubits that you're going to prepare in some kind of state. Let's just say for argument's sake, I'm going to prepare them in the zero state. So they're all zero. And then what I'm going to do is that I'm going to apply

quantum gates to these guys. Okay. And what that means is I'm going to poke this cubit a one way. So it's going to maybe flip from a zero to a one or it's going to flip into halfway between zero and a one. And then this guy's going to talk to its neighbor, who's going to talk to his neighbor. And all of these quantum gates are basically pokes in some way of each of the bits that I have. The key bottleneck is the following. Okay. My gates and my poking. have to be faster than how long it takes for my cubit to forget itself. Here's what I mean by that. In a transistor, if I turn it off, it's going to stay off.

It's off. Unless like a cosmic ray comes in and like, you know, some proton comes in and knocks exactly that transistor from a zero to a one, it's going to remain off. And I can reliably come back and it'll remain off. This is how data works, right? Why is my data? How do I store stuff? Right. This is the transistor is in the, or like whatever thing is in the zero and the one is going

to be in the zero and one forever. It's not a transistor. It's solid state or whatever. But at the end of the day, I have some time horizon that is way longer than my computation. Yes. That I know can reliably remember if it's a zero or a one. In quantum mechanics, that's no longer the case because these qubits are made out of, incredibly small technologies.

Okay? And so there's a lot of forgetting that happens. I prepare my qubit in a zero state. For example, let's take the extreme case of an electron that's spinning in one direction. And that's my zero. If it spins in the other direction, that's my one. Well, I need this thing to remember that it's spinning in that direction. But if there's a bunch of stuff around it, it's going to start poking it, right?

There's a bunch of atoms and a bunch of little magnet. fields that the atoms create that are going to try to change the way that my electron is spinning. So if my electron is a cubit where spinning down is zero and spinning up is one, and I prepare it in the down, it's not going to take very long for that electron to just get influenced by all of the stuff around it and forget that it was a zero in the first place. This is called the decoherence time. Okay. And one of the central challenges is, one of the central challenges in creating a workable quantum computer is to make the decoherence time way, way longer than the algorithm time.

So this actually makes total sense. The point is you don't actually have like a workspace that is active for long enough for you to do like valuable computation to get an output because the decoherence time is so short. similar to our first story because we're at a scale now where the little stuff changes in very, very, very small things that might not have mattered in a classical system. Yeah. Matter a lot in this quantum system in the same way that like under five mock, you don't have to worry about the little stuff.

But above five mock, you have to worry about stuff in a different way. Exactly. Or the details matter. And so if you can increase the decoherence time, that means I can run a long running algorithm. It's like a context window and AI. Yeah. Exactly. Like you have more time to do the thing you want to do before

forgets. Yeah. Before it forgets, right? Yeah, yeah. And there's ways to do this to get around it. There's stuff called error correction where what you do is you like replicate the qubit multiple times and then you have like a bunch of cubits retain that information. So if

one of them forgets, you can have like the other guys. But like at the end of the day, a hardware solution is is king. It's not clean. It's not clean. Right, right. You have to do all of these contingencies.

If you have a hardware solution, well then you can implement that software solution with the hardware solution and even multiply. Correct. Beyond, beyond like what you were. So the hardware is always king. The biggest fundamental limit is still at the hardware level.

It's still at the hardware level. Exactly. And so there's different kinds of cubits. I just gave an illusion of the electron being a single cubit, right, The one that we're talking about is actually pioneered by the 2025 Nobel Prize in Physics by these three gentlemen, John Clark, Michelle Deverey, and John Martinez back in UC Berkeley. They came up with this idea of macroscopic quantum tunneling, right? The idea was to make a quantum circuit, a tiny little circuit that had something called a Josephson junction.

Yes. And then you have on the order of billions of electrons that behave like a single quantum unit. that are tunneling from one to the other, from one to the other, right? We had a great deep dive on this during the Nobel Prizes. So it is a phenomenally well done deep dive. So please go check out our Nobel Prize episode on this because if you're curious about this concept of the superconducting circuits with Joseph from Junctions and macroscopic quantum tunneling,

it is really fascinating. And we have, I think, one of the best explainer videos out. that's available. We want an award from TikTok. TikTok did give us second place for our science content because it was so good. And I just was so fascinated by it.

Please watch that video. Exactly. So these guys sort of, you know, went and created that thing. Right. And IBM and Google now use something called the Transmon design, which is a charge cubit. It's basically their design,

but they've attached a large capacitor on the side. And they've made it insensitive to charge noise. But this is basically the qubit that they're using to make the quantum computers that they talk about. So currently there's not a lot of quantum computers that are being built. There's very few amount of entities that are doing so. The ones that are, like you're saying with IBM, and they're using this particular structure in order to be able to do the process that we're talking about, which has existed prior to the paper.

Yes, exactly. Like there's a platform that everyone's using saying, this is kind of the way we got to go, everybody. This is the current state of the art. Yeah. And that's what it is. Yeah, yeah, exactly. But those guys had this problem of decoherence time.

This new paper that's come out in nature, yes, millisecond time skills. They're getting their stuff all the way to milliseconds. Okay. And this is a victory against the materials that we're using to actually create that Transmon Cubit. And this millisecond time scale is about three times the timescale that was there before, the record. And it's 15 times what is the industry standard.

Okay, got it. Okay. It's very long. It is an order of magnitude. Order of magnitude. Better improvement upscale. Got it. Exactly.

Yeah. And what they're doing is they've, the problem is, as I was saying, right, there's these tiny defects that happen everywhere else that sort of make my cubit forget what I am. If it's a zero, it'll just go halfway. If it's a one, it'll go halfway. There's all these glassy materials. There's oxides, there's substrates that happen when I construct my chip that is going to make the decoherence happen. And so just to kind of zoom in on the point. The point is in the process of generating these chips, we have to make things. at these incredibly small scales.

Our engineering capabilities are quite good, but there are still levels of imperfection or just that the scales are so small that things happen in ways. Yeah, this is at the tens of atoms, hundreds of atoms. So the slightest little thing is going to, again, take our gate of our decoherence time and shrink it with any amount of 10 to the negative, Yeah.

Big number imperfection, which is like barely anything. Yeah. Am I understanding correctly? But to the quantum world, it matters. It's everything. Okay. You know?

Yes. Yes. So that is one of the core sources of the decoherence time problem. Yeah. Is all of these little imperfections that are coming in. And so there's been a history of trying to make things better. Right. Okay.

When it comes to getting longer and longer decoherence times. The first platform was to try and use niobium and aluminum. The sapphire, actually, Sapphire is just aluminum oxide. And that was being used. Instead of niobium, in 2021, there was a breakthrough. The same group, actually, instead of niobium, they used tantalum. Okay. On the sapphire aluminum oxide.

And there they got up to about 300 milliseconds. Okay. Okay? Yeah. Sorry, 300. 0.3 milliseconds. So 300 microseconds. Right?

And then finally now, they've challenged this bottleneck. And instead of using Sapphire, they're using high resistance silicon. So extremely pure silicon that is high resistance. And what's happening is you're superconducting stuff that's happening in the qubit. It's no longer dissipating energy into the substrate itself because the substrate has such high resistance. One way to think about it is the. energy just doesn't want to go there. Yeah, yeah, yeah.

And so it's staying within the, the, the part of the circuit that actually matters. They've locked down their border. Yes, yes, in some sense, exactly. That's exactly right. They've eliminated this bulk loss. Yep. And using tantalum, which is something that not a lot of people use, but these guys were using it, tantalum turns out to be much better than niobium and aluminum.

Okay. So it's this combination of tantalum and silicon. And that was super non-trivial for them to actually grow. Right, right, right. Because you've got silicon and you've got tantalum on top. There's all this, like, chemistry that's happening. This is, like, kind of weird. They had to use a really ultra-high vacuum before they were using just high vacuum.

And it wasn't enough. And it wasn't enough. Yeah, yeah. So they had to use ultra-high vacuum so that when they were depositing this stuff, no weird oil was getting in, nothing like that. When it interacts with the air, nothing weird is happening. it's been a process that's been happening for quite some time. But at the end of the day, they beat that millisecond barrier. And they got to the key, the point is like they had to cook up like Breaking Bad, like Heism,

they had to cook up the material that actually went into the thing itself, the chip itself, that is being now used to do this process. Yeah, exactly. It's like completely novel. It's not like you can just go like, hey, NVIDIA, like, hey, NVIDIA, like, hey, you know, TSM. No. And then they were doing it.

They had to, which like, I want to like make that. Yeah. Like that's a big challenge. It's multiple groups in the university. The chemistry department was involved. The electrical engineering department was involved. Like there's all these different players that were like, oh, actually I'm really good at tantalum. Yeah.

Right. And I'm pretty sure you need tantalum. Right. Right. And then somebody else was like, put this on the silicon and you're going to need a higher vacuum. You're going to need a higher vacuum. Yeah. Someone was like, you're going to need more accountable.

Yeah, yeah, yeah. And there's all of these different, different factors that are coming in in this ecosystem at Princeton to create this qubit. And this cubit is amazing. It's got the best one, the best one had 1.7 milliseconds. That's crazy. And to tell you just how big this is, you know, before you were getting to 0.3, the best one is now five times. And that 0.3 was in a lab setting. It's not at the industrial setting. Okay. So you're getting, you're getting, you're getting, you're getting, almost 20 times to 25 times the industrial setting of stuff. Right? And you might be thinking, you know, a millisecond, that's a thousandth of a second. Thousands of a second is not very long. Well, the gates, right, the stuff that we're using for quantum stuff is on the order of tens of nanoseconds. So you can fit in a lot more stuff in one millisecond now. A nanosecond is a hundred thousandth of a millisecond.

A millionth, sorry, yeah. This is actually really significant because it's also because it's like quantum, like when you're doing, it's parallelized, right? So it's, it's the volume of what you're actually doing. It's not like every millisecond that goes by because you're now parallelized in what you're actually doing, like the actual amount of work. Yeah. The way we think about it in a classical sense. I don't know if I'm explained saying that correctly. You're exactly right.

Like the, how do we say it? The amount of stuff you can do with this. The amount of token output is not just like the same as you would get in a millisecond on a classical system. No, no, not at all. The token output is like running chatcheeb-t paralyzed n number of times. And all of those token outputs get you to your ideal whatever app. I'm just trying to talk about how that millisecond difference is extremely meaningful. Exactly.

And the other thing is, you know, getting, let's say, 20 times longer. Yes. In this decoherence time. Yes. Adds up exponentially. Because here, this is a physical error, right? This is something that's happening at the hardware level, right? So all of your error correction algorithms can now take advantage of the fact that the thing that they are doing,

the error correction on is itself not that bad. Yeah, yeah, it's less error prone. Yeah, right, right. It's more, it has higher efficacy. So, so you're adding on exponential on exponential. Yes. Right? And it's going to, it's going to be a really big thing, I think.

The other, the other huge thing about this is this chip, which is it's, it's just an upgrade on the industry dominant Transmon architecture. Okay. Which is the same thing that IBM and Google have been using in the, their current architecture. Right. Right. The recent paper that came out of the Willow quantum chip in Google, it's using the same architecture. Okay. So the thing is, this is something like I can take that out. Yeah. And I can put this thing in. Yeah. All of the, outside of the chip itself, there's all of the other, the cooling system, like all of the other infrastructure. Yeah. The way I talk to the chip. All of that. Yeah. They can hot swap the chip. Exactly. It's not a new, it's not a totally new, uh, chip design that requires a refactoring of the rest of the infrastructure. Exactly. That's really mean. That's huge, actually, right? Because, because now this is scalable. Yeah. It's industry compatible. Right. And so, and so if Willow just gets this design,

it's going to be a thousand times better. And Willow already is like making headlines. So imagine what can do with this, right? It's really bringing us closer to that future, possibly of where something scientifically standard could be happening with quantum computers within the next decade. You know? And I mean, there's, again, everyone always talks about all the implications of quantum computers.

The one that still gets me most excited is like the ability to create simulations and models with higher fidelity. Yeah, yeah. Coming back to our first story. New materials. Yeah, right, right. And like the things you can then... Yeah, instead of trying a thousand different materials

to see which one is the room. temperature superconductor. I can just like simulate a bunch and then be like, oh, these are 10 good candidates. Let me try making those. You know? Because like alpha fold was a lower complexity problem to like do all the folds that proteins can do. Yeah. I mean, it's still an incredible.

Not minimizing. Not minimizing. But we've already crossed that bridge. So you know what I'm saying? You already got the Nobel. That's in the past. We're trying to go orders a magnitude. Yeah, yeah, yeah.

Now we're doing quantum mechanics, motherfucker. Which is like, it's a big, that's a big, these, all of these stories are actually pretty crazy. Yeah. Like, so, so I think this is, this is huge. The other thing is, they're using silicon, right? Instead of sapphire. So instead of aluminum oxide, they're using silicon. Well, we're as humans incredible at making silicon.

We have the whole, the whole industrial infrastructure for that is actually like prime. Yeah, so scalability in terms of that is going to be huge. That makes sense. Right. That makes sense. Yeah. we can piggyback on this trillion dollar semiconductor fabrication process. This is crazy.

Right. Yeah. And that's a photo of one of the grad students probably posing with it. Yeah, they're like, we don't need your face. We need your beautiful hands. Yeah, yeah, yeah. You're a hand model. Yeah, put the gloves on them.

Yeah. This is expensive. Yeah, exactly. So, yeah, very, very cool. I think it primes the new chips for industrial scaling. we could be getting we could be getting ever closer to that dream.

So now I can read the title the millisecond 5x 3 to 5x longer amount of time you can actually run and keep things in memory for lack of a better term. Transmon the platform that is already industry standard that's used and so it's not like net

new and everyone has to change everything. Cupid breakthrough with the type of thing we do, the tantalum silicon cubit out of princeton in nature we're still number one I don't care what nobody says yeah

we are though because there was a there was a thing that um it took the US news rankings and all these other rankings and then averaged it out oh yeah and we were number one yeah look I'm just we talked about our leaderboard that's coming in our last episode

that leaderboard we're going to create our own metric yeah and make sure you know It will be, the methodology will be public. Yeah, yeah. It won't be a black box like all these other AI models. And the asterisk will be Princeton is all this number.

And we'll just rank based on the facts, you know, as we like them. Yeah. Three super, super six stories this week. We talked about hypersonic aerodynamics. Yep. Above Mach 5. Very interesting. Stevens, Stevens Institute of Technology in New Jersey.

That was in nature of communication. We followed that up with some microbial life in the deep sea, not near hyperthermal vents or anything. This is in a difference. This is cold. This is cold. The title was Life Found and Place Scientist Thought Impossible out of University of Bremen. There were biomarker evidence for serpentineate chemo synthesis. Yes.

Yeah. In the Mariana's forearc, communications, nature communications, earth and environment. and we ended with an incredible quantum breakthrough, millisecond transmon cubits, out of Princeton, in nature. I just, I mean, the stories we've covered this year have just been. And again, it's tough because we weren't doing this in previous year. So it's like, yeah, we don't know. We have no baseline.

Yeah, I mean, I'm learning a lot doing this show. It's pretty cool. It's incredible. I just hope people recognize and understand how incredible some of the stuff we're doing all the time is. Yeah. I mean, as humans, we're so sick. We're sued. We're so sick.

We have problems. Okay. Yeah. Yeah, we get it. Those are like individuals and like groups that identify with problematic stuff. But when we, when it comes to like science and like resilience and like just like our ability to like chipping away at that cave of the unknown. Yes. You know?

It's just amazing. It's incredible. I'm Lester Nare as always joined by my co-host. and our resident PhD Krishna Chowary with another incredible episode. We will see you all next week. This is from First Principles. Yamava Resort and Casino at San Manuel is California's number one entertainment destination for today's superstars. Catch the Jonas Brothers return to the Yamava Theater stage on April 30th,

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