Plain English with Derek Thompson - The Year's Biggest Breakthroughs in Science and Tech (Feat.: OK, But Seriously, What Is Quantum Computing?)

Episode Date: December 31, 2024

Our final episode of the year is also my favorite annual tradition: conversations with scientists about the most important and, often, just plain mind-blowing breakthroughs of the previous 12 months. Today we’re talking about "organ clocks" (we'll explain) and other key biotech advances of 2024 with Eric Topol, an American cardiologist and author who is also the founder and director of the Scripps Research Translational Institute. But first, Derek attempts a 'Plain English'-y summary of the most confusing thing he's ever covered—QUANTUM COMPUTING—with a major assist from theoretical computer scientist Scott Aaronson from the University of Texas at Austin. If you have questions, observations, or ideas for future episodes, email us at [e-mail]. Host: Derek Thompson Guests: Scott Aaronson and Eric Topol Producer: Devon Baroldi Learn more about your ad choices. Visit podcastchoices.com/adchoices

What's up, everybody. Chris Vernon here and welcome to a new season of the NBA and the mismatch. And huge welcome as well to my new co-host, Dave Jacoby. I can't wait to link with you twice a week every Tuesday and Friday right here on the mismatch to break down everything that's happening in the league. Who's playing well, who we loved, who we loathed, trade rumors, team dysfunction. We've got you covered right here. So follow us, subscribe, and hit us with those five-star ratings on Spotify or wherever you get your. your podcast. And also don't forget to follow us on social media. That's at Ringer NBA and check out the full mismatch episodes with the two handsomest podcasters in the history of podcasting right on

the Ringer NBA YouTube channel. Today, our final episode of the year is also my favorite annual tradition. Conversations with scientists about the most important and often just plain mind-blowing breakthroughs of the previous 12 months. Today we're talking about the key biotech advances of 24 with Eric Topal, an American cardiologist and author who is also the founder and director of the Scripps Research Translational Institute. But first, a breakthrough in quantum computing. This past December, as you might have heard, Google announced that its new quantum computer, based on a chip called Willow, solved a math problem in five minutes that would take one of the fastest supercomputers roughly 10 septillion years to crack.

For context, 10-septillion years is the entire history of the universe, about 14 billion years, repeated several trillion times over. The achievement was so audacious that some people speculated that Google's computer worked so fast because it was simultaneously performing its calculations in parallel universes, thus confirming the existence of a multiverse. Now, maybe the idea of a computer working in multiple universes at the same time makes you feel queasy, uncomfortable. Maybe it makes you feel thrilled and awestruck.

I'll be honest with you. It kind of just makes me feel confused. And even a little annoyed? Quantum computing doesn't make sense to most people, and to be quite blunt when I read the news about Google, it made no sense to me. Typically, when I've heard the term quantum computing, I've felt several things at once.

Total bafflement about the concept, a bit of annoyance about an idea that seems like a lot of high-tech hype, and a strange feeling that's something like intellectual jealousy. It's kind of like maybe there are people in your life who have fancy tastes in movies, and they tell you about some 1970s French film that they consider the best movie ever made. So you turn on the movie and 15 minutes in, you're like, this is gobbledygook. What the hell are these movie hipsters talking about? This movie isn't even boring.

It actually makes no sense. Okay, that is how I've historically. historically felt about quantum computing. But I don't like to feel this way. Many of the smartest people in the 20th and 21st century have been awed by, obsessed by, inspired by the promise of this thing. And I want to feel their sense of awe.

I want to appreciate their sense of wonder. So today's episode begins with my attempt to do just that. I cannot promise that this is the single best summary of quantum computing that exists in the world. Actually, I can probably promise you that it's not. But after reading several books about quantum and after speaking to several physicists and computer scientists, this is my best attempt to help folks like me feel the awe

that the smartest scientists feel when they think about this machine. So the best way to explain the value of a quantum computer, I think is to explain the origin of quantum mechanics. In 1687, Isaac Newton published his Principia Mathematica, maybe the most important work in the history of physics. In it, he laid the groundwork for so-called classical physics or classical mechanics. Newton came up with precise rules for how everything in the universe moves in response to everything else.

His theory was beautiful because, among other things, it explained reality as we experience it, the rise of the sun, the orbit of the moon. Even better, it explained the most quotidian details of our life to perfection. When we throw a baseball into a windlass sky, it falls in an arc toward a friend's glove, and that beautiful parabola can be exquisitely determined by knowing just a few factors, like the velocity of the ball and the force is acting on it. Classical physics is so intuitive that even its most sophisticated, ideas can be turned into thought experiments that a child could understand. For example, in the early

1900s, Albert Einstein famously overturned our understanding of gravity by proposing that it was a warping of space and time. In his biography of Einstein, Walter Isaacson explains this theory of gravity in one simple image. Quote, picture what it would be like to roll a bowling-bole of gravity. To roll a bowling onto the two-dimensional surface of a trampoline, then roll some billiard balls. They move toward the bowling ball, not because it exerts some mysterious attraction, but because of the way it curves the trampoline fabric.

End quote. How lovely is that? Gravity is not a force that lives inside of things. It is a field that permeates everything. Gravity is the infinite trampoline. that holds every atom in the cosmos within its bendy frame. This is the point I want to emphasize about classical mechanics, because it's going to come back to bite us when I talk about quantum computing.

Newton's theories are beautifully intuitive, and classical physics gives us hope that when we look at the world, we see things as they actually exist. And then, in the early 1900s, scientists gradually realized that this was wrong. The world we see is not reality as it actually exists. In this period, between about 1900 and 1935, scientists with names you might recognize, Plank, Bohr, Heisenberg, Schrodinger, and, yes, Albert Einstein, realized that when we moved our attention from huge visible things like planets to smaller visible objects like baseballs, Newton's formulas,

still worked beautifully. But when we shifted our attention toward the smallest things, subatomic particles like electrons, particles of light called photons, the rules of Newton completely fell apart. Compare, for example, the rules governing

a baseball and a photon. If you toss a ball at a brittle window, you can predict what will happen next. If you toss the ball very soft, it'll bounce back. If you throw the ball quickly, the window shatters. Classical mechanics accounts for all of this perfectly. F equals MA, force equals mass times acceleration. A harder thrown ball hits the window with more force. It's obvious. Newton's world is a deterministic universe, all tennis balls and brittle windows. But what if you fire something much, much smaller

at a window, like a particle of light or photon. As the physicist and author Brian Green explains in this book, The Fabric of the Cosmos, if you're wearing a pair of sunglasses, there's a 50-50 chance that a particular photon reflected toward you from, say, the surface of a lake will make it through your polarized lenses. When the photon hits the glass,

it seems to randomly choose between reflecting back and passing through, as if some god of light is flipping a coin. It gets weirder than that. Before the photon hits your sunglasses, and you see what actually happens. Quantum mechanics says it will exist in a fuzzy overlap or superposition of both passing through the glass and being reflected.

Superposition is the idea that a quantum system can exist in multiple states at the same time until it's measured. and these quantum states are described by what scientists call amplitudes, which are related to the probability that will find a photon in any particular place when we look for it. It gets weirder still, just as you're ready to jump in the ocean. When light reflecting off the surface of a lake hits your sunglasses, we're talking about not just one photon, but countless photons, many of which have been entangled with another light particle.

The destiny of these photons making contact with your sunglasses is intimately connected to those entangled photons 10 miles away or 10 light years away, what Einstein memorably called spooky action at a distance, or what we more simply call entanglement. So the world, as we know it, is in fact two worlds. There is a world of planets and baseballs and predictably broken windows, which looks and feels and sounds like our solid reality, but that solid reality is underpinned by a second world, a spooky world, a quantum world

of probability or amplitudes, of superposition, and of entanglement. If you feel a bit lost, that's okay. Some of the smartest people who ever lived, like, say, Albert Einstein himself, hated many of the implications of quantum mechanics. God does not play dice with the universe, Einstein famously said, And he challenged his friend Irving Schrodinger, one of the giants of quantum mechanics, to make sense of this world

that he and the other quantum scientists were building. Schrodinger, for his part, actually agreed. He thought the implications of superposition were kind of ridiculous. In a letter back to Einstein, Schrodinger came up with a funny thought experiment, a closed box with a cat, which is rigged with a poisonous contraption and a trigger.

Before you open the box and make a measurement at the cat state, Schrodinger said, the math of quantum mechanics seems to hold that the cat is both alive and dead at the same time inside of that closed box. This thought experiment is now known

as Schrodinger's cat. It's a very famous visualization. It's often used as a shorthand for two things being true at the same time, or mere uncertainty. To be honest, I always assume that Schrodinger's cat was Schrodinger's way of explaining quantum mechanics to a dummy like me.

But that's completely wrong. It turns out that Schrodinger came up with his thought experiment, not to persuade his peers that he was right, but rather to commiserate with Einstein about the fact that quantum mechanics was too absurd to be taken seriously. Here's Scott Aronson, a theoretical computer scientist at the University of Texas Austin, with a slightly more technical explanation of Schrodinger's cat.

Trottinger's cat was his attempt to sort of construct a reductio ad absurdum showing why this couldn't possibly be the whole story. Right. He said, suppose that we had all the particles in a cat entangled with each other, right? So that, you know, with some amplitude, you know, the entire cat is dead and with some other amplitude, the entire cat is alive. And now we've we've perfectly isolated this cat from the from its environment that that's what the box is for. And now, you know, we would have to say that until something external makes a measurement, you know, to learn the state of the cat, you know, the actual truth is that it is in a superposition of the alive state and the dead state.

We can't say that it's one or the other. And now he said, okay, but that is since that is an obvious. obvious absurdity, then we conclude that quantum mechanics can't be the full story. But as far as we can tell, this is the whole story. No one has managed to put a cat in a superposition of alive and dead or anything nearly that big, but they can effectively do the Schrodinger's cat experiment with bucky balls, for example, with, you know, molecules involving hundreds of or even thousands. of degrees of freedom of particles.

They can put this whole molecule, which is, you know, okay, it's still tiny, but it's huge by the standards of, you know, of an electron, let's say. They can put the whole thing into a superposition of traveling through one slit and traveling through the other slit. And then by looking at the interference of the amplitudes that we were talking about before, they can prove that it really was in that superposition state, right? It wasn't just in one state or the other. other. The math of quantum mechanics is sound. At the tiniest level, reality is not fixed. It is probabilistic.

It is a superposition of quantum states whose probabilities are described with amplitudes. But what the hell does that even mean in, say, plain English? Well, one interpretation of quantum mechanics says that there is no definite state of physical reality until we make an observation. It is by opening the kitty box and looking inside that the cat's state becomes our reality. So this seems like, you know, a check, like maybe the most basic challenge to our conception of reality that physics has ever coughed up. This is saying, no, you know, when, when, when, um, physical systems are isolated from us. They don't have a definite state at all, right?

They exist in the superposition of different states. And then, you know, the next question becomes, is there any limiting principle to that, right? There is a strange kind of poetry to this. The world only comes into being when we look upon it. Or as the Chilean novelist Benjamin Labatou once put it, quote, like the moon in Buddhism. A particle does not exist.

It is the act of measuring that makes it a real object. It is kind of a beautiful idea, except it might not even be true because there's another interpretation of quantum mechanics that's even trippier. Once you have accepted that, you know, quantum systems exist in these superpositions of states,

the next question becomes what limiting principle is there that is going to prevent that from being true at the scale of everyday life, right? And now the classic answer that, you know, Boer and Heisenberg and the other founders of quantum mechanics would give was basically, well, look, there is a quantum world, you know, that's the microscopic world. And then there's the classical world that we live in, right? And when you make a measurement, when you ask, you know, your photon where it is, then in effect, the classical world is reaching down into the, the quantum world, you know, interrogating it, right? It is then forcing the photon to make up its mind, you know, about where it wants to be.

The same way that opening the box forces the cat into a position of alive or dead. But then, you know, that answer, I think, was bound not to satisfy people for, for very long, because then they said, well, wait a minute, aren't we made of atoms also? you know, and aren't our measuring devices made of atoms? Don't each of those atoms obey the same laws of quantum mechanics? So, like, where does the buck stop, right? Like, at what scale do we pass from the quantum world to the classical world? Back to Schrodinger's cat.

When we open the box and see the cat is alive, what happens to the other version of the cat? Right? Where's the dead cat? The dead cat was just here, right in front of us, so to speak. where is it now? In 1957, an American physicist named Hugh Everett had an idea. Everett's simple but radical answer to that was, well, you know, the whole universe just continues to evolve in a superposition of states.

And when you measure, this is just a special case of entanglement. This is just a situation where you, you know, your measuring apparatus, you know, the atoms in the room, you know, the air molecules are all becoming entangled with the state of the system that was measured, right? That state is getting recorded in all of these degrees of freedom, which you could describe as, well, you know, the whole universe is now splitting into two different branches, you know, in one of which you perceive one outcome and in the other of which you perceive the other, okay? And, you know, the equations tell you that those two branches will then continue to evolve independently from each other, right?

But there's sort of nothing in the Schrodinger equation that would make one of the branches disappear. This is called the many-world's interpretation. And if you think it sounds like a multiverse, that's because it pretty much is a multiverse. In Everett's theory, the lifespan of a person like you or me is a branching tree in time. Every day, multiple copies of you

are constantly being created as you come into contact with other quantum systems. Countless parallel universes keep branching out over and over even though we only have the experience of one of those branches moving through time.

Of course, this sounds like absurd science fiction. But it's actually the opposite of science fiction. In a weird way, the many-world's interpretation, the idea that we live in a multiverse of constantly branching realities

might be the most straightforward way to make sense of quantum mechanics. So in some sense, you know, it's not that you're positing multiple universes. It's just that you are taking the Schrodinger equation completely literally. So you might be thinking at this point. Derek, I thought this was supposed to be an episode

about quantum computers. I don't see any computers here in this picture. Good point, imaginary listener. So let me sum up. before I take us there. For 100 years, two things have been true about quantum mechanics. First, it is the most fundamental truth

we know about the cosmos. The scientist, Sean Carroll, has called quantum mechanics, quote, the deepest, most comprehensive view of reality we have. Second, quantum mechanics is too complicated to fully understand. Today, 100 years after the quantum revolution,

we can't use it to say, make better drugs, or design stronger materials, or simulate quantum systems in a way that would tell us what this thing we call reality even is in the first place. The physicist Richard Feyneman once said, quote, I think I can safely say that nobody understands quantum mechanics. End quote. Feynman was no fool.

He might have been one of the smartest people to ever live. The title of his biography is, literally genius, colon, the life and science of Richard Feynman by James Glick. Fantastic book, by the way. If Richard Feynman says the quantum mechanics is too weird to fully understand, I think it's forgivable of us to agree with him. So what do we do with a truth so bewildering? Consider a strange allegory.

The astrophysicist Adam Frank has frequently raised the possibility that scientists will one day discover ancient alien civilizations, not by looking out to the heavens, but by digging down here on Earth, that buried deep in this planet's fossil record, we will find evidence of ancient alien life. Now, imagine what might happen. If archaeologists discovered evidence of an advanced alien civilization in a deep dig,

volumes and volumes of pages produced by this superior, intelligence. But then we realized this alien language was impossibly complex. Words were not spelled out linearly. Sentences did not follow sequentially. Instead, in this alien codex, each page of text included thousands of mysterious letters in a grid, like looking at a vast crossword puzzle in a foreign script. It took our best minds years and years to learn a word here, a word there, but it was taking too long to fully crack the alien cipher. And then someone came along with a clever idea. What if we built a special code-breaking machine to decipher sentences, paragraphs, entire

books from this ancient alien language? What if we built an alien computer? Well, in a way, the discovery of quantum mechanics really was like the discovery of a language that is alien to our intuition. But this alien language doesn't come from another planet. It comes from our own digging. We found it here, on Earth, nestled in the fabric of our own reality.

And while our best minds have made important progress here and there, we are fundamentally blocked on its most important implications. We don't know how to use it. We don't know how to fully model it. we don't even really understand what quantum mechanics means. And so in the 1980s, some of the smartest physicists in the world came up with a crazy idea that if the alien allegory clicks for you, you'll realize isn't that crazy at all. If we want to really understand quantum mechanics, they said,

what we need is something beyond human intuition. we need a special computer that speaks the language of quantum mechanics like a native. A quantum computer. So a quantum computer is just a computer that would operate according to the principles of quantum mechanics that we've been talking about.

So it's an idea that several people arrived at independently in the late 70s and the early 80s. One of them was Richard Feynman himself. Feynman was mostly interested in the practical question of how do you simulate quantum physics on a computer, right? And since the early days of electronic computers, people had been trying to do that. But the trouble is that they could only simulate very small quantum systems. Okay. And why is that? Because once you, you know, remember that I have to keep track of all of these amplitudes,

for like a bunch of entangled particles, right? The number of amplitudes will in general grow exponentially with the number of particles. Between the 1920s and 1950s, physicists and mathematicians were winning awards left and right for their ability to extend our understanding of subatomic particles and quantum mechanics. But fundamentally, what we want from science

is practical applications. We want chemists to model a quantification, quantum system in a way to give us better drugs. But it was simply too hard to track all of the variables. The chemists and physicists, you know, recognized this from the, you know, from at least the 1950s as a practical problem, right? And so they were mostly focused on, you know, inventing like tricks and approximation methods to get around this, right? And, you know, And the better approximation methods like density functional theory were awarded with Nobel prizes. But then a Feynman posed the question in the early 80s, is there a general way to get around this?

Right. Like, does simulating quantum mechanics on a classical computer inherently require an exponential blowup in at least some cases? And then he had this amazing proposal that if it does, well, then why don't we make this lemon into lemonade? In other words, if nature is giving us this intractable computational situation because of this exponentiality of amplitudes in quantum mechanics, then why don't we build our computers to exploit that very same exponentiality, right? why don't we build computers out of qubits instead of bits and let them evolve into entangled states so that they too would be able to take advantage of this. A qubit or quantum bit.

So traditional computers, like your smartphone or my laptop, process information as a parade of binary switches that flip between one and zero. Quantum computers use qubits, which can represent both a one and zero simultaneously, thanks to our friend superposition. As you add more cubits, the computational power grows exponentially.

If I have one qubit, that's two amplitudes, right? Amplitude to be zero, amplitude to be one. Okay, but if I have two qubits, now I need four amplitudes. I need an amplitude for zero, for zero, for zero, for one, for one-one, for one-one. Okay, if I have three cubits,

now I need eight amplitudes, right? 10 cubits, 1,000, more than 1,000 amplitudes, 20 cubits, that's more than a million. And now if I have 1,000 cubits, that's 2 to the 1,000 power amplitudes, which is more than you could explicitly write down in the whole observable universe.

So what would this computer, this machine that is fluent in the alien language of quantum mechanics, actually be useful for? In the 1980s, the interest in quantum computing was mostly theoretical, and outsiders could be forgiven for thinking the quantum computer was really just a name for a very, very, very fancy calculator. But in the 1990s came a breakthrough that put quantum computing on the map in a whole new way. You know, the big breakthrough there came in 1994 when a mathematician named Peter Shore,

then at Bell Labs, discovered a fast quantum algorithm for finding the prime factor. of huge composite numbers, right? And why do we care about that? Well, you know, most people have seen that problem, you know, at least in school, right? The ancient Greeks, you know, thought about that problem, of factoring. But today, it is especially important because it underlies the security of most of the encryption that protects the Internet, right? anytime you order from Amazon or whatever you or visit any website with HTTPS, your information, including your credit card number and so forth, is being protected by

what's called a public key encryption system, right? That the wonder of these systems is that they let us exchange secure messages without having to agree in advance on a secret key. didn't have to meet with someone from Amazon in a dark alley at 3 a.m. to agree on the key, right? But the Achilles heel of these systems, which were discovered in the 1970s, is that if anyone could find a fast way to factor huge numbers, for example, or solve some related problems in a number theory and algebra, then they could break these systems. Okay. Now, for half a century, no one, at least no one that we publicly know of has found a fast method to break these systems using a classical computer.

But what Shores showed in the 90s was that if you build a quantum computer, then you could quickly factor like 2,000 digit or 10,000 digit integers. And therefore, this whole scheme that we use to protect communication. over the internet would be broken, would no longer be secure. And thus, quantum computers went from being a curiosity of physicists

and theoretical computer scientists to a technology with possible geopolitical consequences. Money started flowing into quantum computing. But still, the engineering challenges were massive. After Shore, you know,

came up with the ideas of, you know, of, well, you know, of Shores algorithm and so forth, and 30 years ago, a lot of people said, you know, this is, this is, this is nice on paper, but no one is ever going to build anything like this, right? You will never be able to control qubits nearly well enough to build this, right? So then it just seemed like an absolutely staggering engineering problem. And, you know, the significance of the present moment is that it seems like we are now, you know, at the very least, the majority of the way to, to solve them, the, you know, the relevant engineering problems, right? So, you know, when I entered

this field, like in the, which was the late 1990s, right, it would be amazing if you could just even just get two cubits to interact with each other with, let's say, 50% accuracy, right? And then, but then at some point, 50% became 90%, became, you know, 99%. And now within the last year, what we've seen is people doing two-cubit operations with 99.9% accuracy. Okay, so now, five years ago, so in 2019, Google demonstrated a chip with 53 superconducting qubits, which they called Sycamore. and they demonstrated like some computation that at least at the time

seemed very challenging to simulate using a classical computer. And now, at long last, let's finally talk about this year's breakthrough in quantum computing. Okay, so now what has happened with the Willow chip this year? So, you know, it's the same kind of thing as their Sycamore chip in 2019,

but now it's got twice as many cubits. It's got 105 cubits, I think, and the cubits stay alive for five times longer. So you sort of cross the threshold where as you do bigger and bigger error correction, it helps more and more rather than helping less and less. Willow didn't just prove that scientists were closer to building an error-correcting quantum computer.

Remember, it solved a math problem in five minutes, that would take the fastest supercomputer roughly 10 septillion years. Now, to be fair, this was a math problem that was gift-wrapped for Willow, designed specifically so that Google's computer would have an advantage, but it's still pretty impressive. And this is where we bring back the many-world's interpretation of quantum mechanics. Many people I saw commenting on Google's quantum computing chip said this achievement was only possible because the computer

could access millions of parallel universes at the same time to speed up the task, right? It solved the problem in five minutes rather than 10 septillion years because it could access like 10 septillion different parallel universes and borrow computing power from all of them at the same time. Pretty trippy idea, but it wasn't clear to me that the people making these comments knew what they were talking about. So I put the question to Scott directly, right? Is that interpretation plausible? Did Google just prove to all of us that we live in a multiverse? I would not say so, no. And the reason is that if you believed in the sort of philosophical argument that quantum computing implies the truth of many worlds, that you should have already

accepted it before Willa, right? And if you didn't accept that philosophical argument, then nothing here should cause you to change your mind. In some sense, like all of them, is like an artifact of trying to express things in language that wasn't designed for it. It's like you can say like the real truth of the matter is that you've had a superposition, right? The world is quantum mechanical, and this was yet another demonstration of the quantum nature of the world, that, you know, that, yes, the true state of a system is this, you know, gargantuan superposition, right? I think that part is undeniable, right? But now, you know, should we describe that in terms of, well, you know, when you make a measurement,

then, you know, the universe splits into all these copies, you know, with all these different, different versions of conscious beings like ourselves, you know, I think, you know, that would be a further extrapolation, let's say. You know, and it's an extrapolation that some people would make, but, you know, they could, they, if they were going to make it, then they should have made it long before this experiment. Now, of course, I'm interested in the philosophy of all this. I want to know if we live in a multiverse, but I'm a realist. I know a lot of people don't give a crap about all that.

They're not remotely interested in the philosophy of many worlds, the possibility of parallel universes. What they care about is, I want drugs that make me healthier. I want materials that make my electronics work faster. I want my energy to be cheaper. So what I really wanted to know from Scott was, what would effective quantum computing mean for people, the economy, by, say, 2030? So the truth is we don't really know, right?

This is exploratory science. I mean, people, you know, in order to raise the amounts of capital that are needed to build these devices. People always try to sort of fit this into the category of, you know, a business proposition that like, here's what it will lead to. I mean, what we're confident of is that it ought to help a lot with simulating quantum systems and, you know, simulating a complicated and tangled quantum systems for which our existing classical approximation methods have failed, right? And that ought to be useful. You know, it is hard to see how it could fail to sometimes be useful to, you know, the battery industry, the photovoltaics industry, to combinatorial drug design, to, you know, any situation where you basically have a complicated entangled quantum systems that you're trying to simulate.

Now, the hard part with quantum computers is that it's never enough to just do something. using a quantum computer, you have to beat the best it could be done with a classical computer, right? And classical computers can fight back, right? Like, it is again and again happen that someone announces, like, with great fanfare. Like, we did such and such using a quantum computer, and it was 10,000 times faster than the classical solution. Okay, but then as soon as people look at it, they say, well, you didn't try very hard to optimize the classical side, did you? Right. And as soon as they do that, then they find that the quantum advantage goes away. I love this answer.

This is the breakthroughs episode, which is to say it's sort of inherently a hype episode. I want people to feel excited about scientific and technological progress. But Scott's closing message is anti-hype. Yes, quantum computing could transform internet security. It could help us design better batteries and better drugs by understanding electron behavior

and subatomic particle dynamics, but the only honest answer to the question, what will quantum computers actually do for us, is we just don't know yet. But after reading books like by Sean Carroll, Brian Green, Walter Isaacson, and after speaking with Scott Arensen, I decided to be excited about quantum computing anyway.

If quantum mechanics is, as Sean Carroll says, the ultimate truth of reality that is yet somehow alien to our intuition and if Scott is right that our existing machines are insufficient to handle the breadth of mathematics

required to speak that language fluently then I say bring on the alien translation device bring on the machine that closes the distance between our puny human minds and the truth the vast and hidden truth of our reality.

We'll be right back. Welcome back. So every year, the journal science, just like this podcast, names its breakthrough of the year. And this year's breakthrough of the year was a new injectable drug to protect people from HIV. In one clinical trial of South African and Ugandan girls and young women, this shot, which is called Lenna Cappavir, reduced HIV infections by 100% in the intervention group. Another trial of people across several continents reported an efficacy rate of 96%. Clinical trial results do not get much more successful than this.

This matters, of course, because around the world, 40 million people live with HIV, and an estimated 600,000 of them die from AIDS-related illnesses every year. This disease has no cure, but a drug this successful that only has to be taken once every six months, that is something we can call incredibly close to an HIV cure, and it happened this year. So to talk about Lena Capavir and the most interesting other things happening at the frontier of biotech, we now welcome back Eric Topol, an American cardiologist and author who's also the founder and director of the Scripps Research Translational Institute. Eric Topal, welcome to the podcast.

Great to be with you again, Derek. This is one of my favorite conversations from last year talking to you about the most significant scientific breakthroughs in the field of medicine and biotech. So I was very grateful when you agreed to do this again. In my open, I talked about Lenna Capavir, which is the new rather miraculous HIV therapy that science named its breakthrough of the year.

before we dive into the subjects that you really wanted to talk about, let's take a pit stop and Lena Capavir. What do you find most important about this therapeutic breakthrough? Yeah, I mean, I think it's got three dimensions that are like wow factors. The first is, of course, the two trials that showed, well, one in, that was 22,000 young women in Africa, 0%? Zero infections? I mean, that's unprecedented. And then replicated four continents, you know, again, 2002 infections. I mean, so it's amazingly effective. You don't see efficacy like that very often. Now, that's one. The second thing, though, is going after the capsid. So all the things that

we do against viruses, like, for example, SARS-CoB2, we go after these enzymes, proteases. The M-Pro is the one that we go after with, for example, Paxilvid. But here, it's the structure that's holding the virus, the guts of the virus, the business party, that basically knocks it out from beginning into the nucleus, going through the nuclear port complex. Or if somehow it gets through, which makes it much more difficult and much more rigid, then it blocks it from being produced. So it's like a double whammy against it.

But the other third thing is it's a precursor to a lot of the new medications we're going to be seeing in the future, which is this once a year or once every six months. So it's kind of amazing. Of course, it's now every six months, probably go to once a year. But we have now a cholesterol lowering medicine that's once every six months. We're going to have blood pressure medicines injectable once every six months or once a year. So we're seeing whether it's because it's low salubes. or whether it's a small interfering RNA, we're not used to having medicines once a year.

So those are the three things I think are really noteworthy about this. And it opens up the ability to knock out the capsid for many other viruses because we haven't been thinking that way. We've been thinking about how do we get to the virus's replication machinery rather than the structure that it's housed in. So there's a lot of other viruses that we could do some real, good stuff with this way. I love that quick review.

Let's just go one level deeper on the third thing that you mentioned. Why is it so significant that you only have to take Lena Capavir once or twice a year? Does it have to do with the fact that with many medications, one of the problems is that

patients aren't necessarily very good at adhering to once a day, once a week, pills, injections, but if it's once every six months, much easier to get on sort of the doctor's schedule and, you know, get that shot. If you miss your appointment by two weeks, well, you're still basically in the window of being protected from this medication still for the entire year. Is it about adherence or is it about something else? Well, adherence, of course,

it varies with the indication. So for HIV, you know, we had these different pills that work really well for prophylaxis, but they just weren't taken. And in that trial, where compared to the six-month Lanakapavir. I mean, they didn't really do much the pills because the adherence is so bad. But, you know, people, because, for example, blood pressure doesn't really cause any symptoms when it's high, for the most part, people just say, I don't need to take my blood pressure medicine. Again, your cholesterol levels, you know, you're not, it doesn't cause symptoms. So adherence to these medications is not great. So if you could just get a jolt that lasts and does the job for a year, you know, these are new, like right now, for example, the hottest drug ever is the Glyp 1 family of drugs,

and it's given once every week, you know, self-injection. And obviously, millions of people are taking it. But there's going to be a Glyp 1 family drug that's going to be, you know, every three months or every six months. And that's going to change things a lot. So that's where a lot of medications are headed. And it's not a vaccine, but it's the level of potency and the duration. You know, it's kind of like a pseudo vaccine in terms of its impact. Right, like a half vaccine.

You're saying Glipp 1. I'd never heard it said Glipp 1. I'd always read it or said it in my head as GLP 1. But these for people listening are drugs like OZempic and Zep bound, which initially came around for diabetes too, but now are widely used for weight loss and a bunch of other miraculous side effects that he'd be having as well. Let's go to some of your favorite breakthroughs of the year, starting with proteomics, proteomics, one of the interesting new omics that are coming online, this one

dealing with the new science of proteins. You wrote a comment in Science Journal on some recent developments in blood tests, which determine our health by studying thousands of proteins in these blood samples. Tell me how this works and what you were most impressed by this year. Yeah, so this is really a striking advance. You know, you don't see these sort of things very often. But basically, because of two different companies, one is Thermo Fisher that has this assay called O-Link, and the other is Somalogic that has an essay called Somoscan. They are getting now between 5 and 11,000 plasma proteins in our blood from a tiny, you know, a couple of microliter. of blood, so it's just a tiny sample. And they're getting these thousands of proteins that are telling us things we never knew about in our body. So for one, that was kind of shocking when it first came out, was the so-called organ clocks. So each of us are all our organs, whether there's their brain, kidney liver, heart, our immune system has an organ. We can track that. Are we accelerated aging?

are we slower than expected aging? And so we used to be, we'd say, oh, somebody, it looks like they're aging quickly on the external, total body wide. Now we can pinpoint really for the first time, what organ, if any, is the trouble spot. And that's great because now we have all these different ways to partition high risk and get all over this so that the person doesn't accelerate, doesn't continue the adverse course of fast aging of a particular organ, organ system. But it's much bigger than that, Derek, because one of the recent studies, they found causal proteins that have never been identified for hundreds of diseases because they also factored in with the so-called technique of Mendelian randomization and knowing the genomics and the

proteins. And then the other thing that's been really striking that caused a lot of attention or stir is that everybody that we saw aging was a linear process. You just deteriorate more as we as we get older. Turns out, no, no. Now there's a few studies that have shown it's really coming in spurts. Your aging is the first bad wave is in your like 35, 45, 45, 5. The second one is around 60-ish. And then you have a third wave that's, you know, right around age 80. So that's another thing that we're just starting to get our hands around, arms around. And more I mean, this is a revolution that people don't appreciate that we're learning about the proteins on our body at a very accelerated pace, and it's going to change the way we approach patient. I want to make this super concrete for people. So the idea is, tell me how this is wrong. I go to the doctor.

The doctor takes my blood. There's an analysis of my blood that's fed into some kind of AI model, right? Sort of like an advanced version of, like, copy pasting, a complicated essay into chat GPT and saying, summarize this for me, pull out the most important points, and the AI will essentially pull out this analysis of the proteins that gives you an age-specific organ clock. So I'm 38 years old, I don't smoke. I do, however, love wine and cocktails, and I walk just enough. So, like, maybe I'm 38, but my lungs are 37. My heart is 38, and my liver is, like, 40, right? There's going to be some kind of error bound there, because chronological age is very

precise, but I can't imagine we're like super duper precise when it comes to like the exact biological age of the cells in our liver. But is that the general idea that this sort of spectrum of organ age measurements can tell the patient and his or her doctor what to focus on as their health is being monitored because you can see which parts of the body are more likely to break down or develop disease in the near future? Is that the so what here? That's the so what. I mean, the only thing, what I'd say is you're right about, you know, plus or minus a couple of years.

That's not, you know, what you're really looking for. You're looking for an outlier, you know, five, 10 year difference. And you're the biologic age of an organ from these proteins versus the chronic age of your real age. So that's what this is about, this kind of age gap in an organ. And it's especially important because, as you know, these, the three big diseases cardiovascular, neurodegenerative, naming Alzheimer's or Parkinson's, and cancer, these diseases take 20 years or more to take hold, to get, you know, take root.

So if you can anticipate that there's an organ that's acting up ahead of time, you know, you can get all over it and go into a tight surveillance and there's prevention things that we would do. And so sometimes these are not related to your lifestyle, like you reviewed, could be your genetics. It could be environmental exposure that you're not aware of. You know, I would liken it to, you know, these days if you take your car in with advanced diagnostics and they can check every part of your car electronically, you know, this is kind of like doing this into pinpointing an issue in your body that you might not be aware of.

Obviously, this needs more work because it's fresh and it has to be validated that when you know this information, that you actually change in that for history of a person's health. in a favorable way. You don't want it to lead to incidental rabbit hole expeditions, that kind of thing. But it looks quite promising because we've not ever seen this type of unraveling new aspects about a person's health before. I'm glad you mentioned that this needs to be validated because I have one question that comes from skepticism, but I have another question that comes from enthusiasm. So let me start with enthusiasm and then go to skepticism. It seems to me like a next step here that would be very interesting is connecting other interventions to the concept of organ-specific aging clock. So, for example, someone could do a study of what is the effect of GLP1 or Glyp1 drugs on organ aging, right?

What is the effect of OZemPEC on the age, so to speak, of various organs that are related to inflammation if GLP1, in fact, has a positive effect in reducing inflammation. That would be fascinating. What about statins? What is the measurable effect of the, of statins on the heart's age-related organ clock? Or what's the effect of, you know, people are talking about ultra-processed meats? What's the effect of ultra-processed meats on organ-aging? What organ ages fastest because of consumption of ultra-processed meats? You mentioned, pollution, environmental factors, air pollution, effect on the age-related organ clock of the lungs. Is this part of the hope that we get a more refined, more detailed, more specific way to talk about various interventions and environmental factors on our internal health because we can use this more specific language to talk about its effect on individual organs and not just, you know, overall mortality outcomes? Yeah, you know, you always process things well and quickly, Derek.

I think the key here is that by knowing this status of a particular organ, that we have lots of actionability to get all over it. And we didn't have this insight before. Now, what's interesting is, as you say, if you were sedentary and you start exercising, do you reverse your heart clock as a great example? We have seen that Glyp1 drugs slow the epigenetic clock, which is a body-wide aging clock. And that's one of the only things besides exercise that can do that.

But that's not organ-specific. So matching up a person's organ at risk, particularly in younger people like you, and also the pairing it with the right types of potential interventions, that's where we're headed with this. That's exactly how it's going to be used in the future. So here's the question from skepticism. this space of aging science has both some science that's held up quite well over time and some science that's passed through various hype cycles. Like I don't even know where we are on telomeres,

for example. I feel like telomeres were incredibly exciting heart science for a while, and then there was like a backlash against telomere length, and now maybe there's like a backlash to the backlash. How does our confidence in the proteomewere's, like, the proteome, of age-specific organ clocks, compare, for example, with our confidence about, say, telomere length as a proxy for age? Yeah, so good point. A lot of the stuff on aging assessments have not really panned out. The telomeres, it went commercial very quickly, and it really has never been shown to change

a person. Why take half a step back? I should have done this in my question. Take half a step back and just tell people, what's a telemere? what do people think telomeres for proxies for? And then what's the current state of the science? Yeah, so the idea was the tips of the chromosomes, the telomeres, if they shorten, that's a bad sign of aging in an accelerated premature fashion.

And so some of the people involved in the early work of telomeres actually formed a company or multiple companies eventually that were selling, you get your telomere length of your chromosomes. and it has no value really. And there are a lot of other aging things that have been hoaxes like, you know, resveritrol and, you know, for all sorts of things, you know, make a long list. But they never got clinical validation. Now, the difference here is, first of all, there's no company on organ clocks. That's good. There's only companies that do the plasma proteins and they're doing it for research purposes.

And there are going to be ways to get your organ. clocks in the future. That's where we have to find out. There's been three major papers now. The first one came out of the Stanford group, Tony, Whiskoray, and Nature, and then two more since they're followed. And these are large populations of, you know, the UK Biobank with tens of thousands of people with 15-year follow-ups. So these multiple publications look very encouraging. We never had that for telomeres or these other interventions or diagnostic tests. But we still have to show that once you have this information, that it makes a difference in people. And that takes time. That's another level of research that's in suspension until we get it done. Right. And to a

certain extent, it seems like it's just inevitably going to take time to learn whether advanced aging of organs correlates with later disease. Because, for example, if you run a bunch of studies on, say, a 25-year-old and say, you know, this 25-year-old has a liver that's 40 years old, well, it's still going to take like 20, 30 years, right, to learn whether or not that matches up with them getting advanced liver disease three decades prematurely. So to a certain extent, it's just going to take some time to learn whether or not the measurement of the age-specific organ clocks matches up with life outcomes, right? That's just inevitably going to take a certain amount of years.

So one key point here is we're no longer just going to assess organ clocks alone. We're going to be using it so-called multimodal AI or multi-layered AI. This is a whole new layer that's now becoming available. And the cost is just precipitously dropping, which is really great. But it will be with our genome, with our environmental exposure, with our electronic health record, with our images and other conventional labs and our social determinants of health, lifestyle. So the point here is that we're not any longer going to rely on one layer. So if you have an organ clock that's showing up a particular issue, you've got other layers

we call orthogonal to corroborate it with, you see. So the work that's going forward now is not going to be like, oh, we're just going to check telomere. No, no. We're looking at the whole holistic package to be able to chart one's health course long before they ever have a condition that they were predisposed or susceptible to. That's really what this is about. This is a new layer, a new dimension of data that we didn't have until recently that is just adding to the mix, that we're not going to assess on its own because that's, you know, old style. That's narrow ice pit. We're going for the broad, you know, holistic story. Let's move on from proteomics to a subject that is near and dear to your heart, pun only slightly intended, which is breakthroughs in cardiovascular health science.

So the stakes here don't need much setting, but I'm just going to set them anyway. Cardiovascular disease, heart attack, stroke, heart failure is the number one cause of death in the U.S. and in most developed countries. We have made really incredible strides here in cardiovascular. vascular disease mortality in the last 50 years, thanks to, among other things, advances on at least two fronts, drugs that manage cholesterol, like statins, and interventions to treat blockages in arteries. But there's another front in this war that you consider highly promising, which is treating inflammation. Before we talk about the advances in treating inflammation this

year, can you set the stage by helping me understand why, inflammation is important and why perhaps something so important has been somewhat overlooked in the last 50 years or so of medical advances in this space? Yeah, I mean, this is actually striking to me is that we're kind of into blockages. You know, oh, the cholesterol is build up and it's now limiting the blood supply and fixated on that and on cholesterol, bad cholesterol, particularly LDL cholesterol. And we're not thinking about the bigger picture is how did that blockage get there. And by the way, it's not just the LDL cholesterol. There's other ways that you can get arteries act up to cause a heart attack. And it doesn't even have to have a blockage.

You could just have a minor so-called plaque where it just cracks or ruptures. So the big miss, as I call it, is this layer of information of how is it. the artery inflamed? Is there a sign that the artery is inflamed? And unfortunately, we've only had these very rudimentary, what I would call really primitive ways to assess that. One of them is called a C-reactive protein, CRP level in the blood. And then there's these other blood markers inflammation that aren't used very much like interleukin-6 or cytostatin. So we don't have a good blood marker. but we do know for example that statins and if you do lower the lDO it tends to lower inflammation and when we fix an artery whether it's with a stent or if a person understill undergoes a bypass

it gets rid of the blockage but it doesn't necessarily do anything with the inflammation that's underlying that caused the problem and heart attacks and strokes uh very commonly involved um the inflammatory process as a underpinning so the story is we've We've not given enough, I guess, recognition, acknowledgement of how important this process is, partly because we didn't have a way to measure. The blood tests are just very shaky. And even with the shaky blood tests, we saw some evidence of Coltazine and another drug in a trial that work, that are not what we would call really primo anti-inflammatory drugs.

So there's a room here to make great headway for preventing heart disease and actually also preventing a lot of strokes too. And that's to take advantage of this process of detecting inflammation, unsuspected, and getting all over it to block the process. And what's happened in the last 12 months specifically that makes you most excited, most optimistic about this space? Yeah. So I consider a landmark paper.

was in the journal The Lancet, and it was from the UK. And they have a way to detect inflammation in our heart arteries, our three major heart arteries, that we don't have in the U.S. So I'm envious, okay? Basically, what they do is they use AI to look at what they call the fat attenuation index of each artery on the image. It's a CT angiogram, which can be done very readily through a CAT scan, with an IV that gives a limited amount of dye.

And so you get this image of the arteries, but unlike what we have in this country or anywhere else, they have in 40,000 people with seven-year follow-up, eight hospitals in the UK. They had the well, so-called inflammation index for each of the three arteries, all those people, with follow-up. And again, when we were talking about earlier, about the

Lennacavir drug having zero events in that large population of young women. Well, this is also similarly striking. And people that had three arteries

inflamed without even any blockage, they had a 30, 29.8-fold risk of a heart attack. And even one artery inflamed, 13-fold risk.

So the point is, is that if we were worried about somebody with heart disease now because of these layers of data, why wouldn't we do this and find out if they have inflamed arteries, or better yet, Derek, why don't we get the proteins nailed down from those plasma proteins, which correlate with this AI determined inflammation from the images? So this is a hot, hot area. That's the wrong word to use because heat and inflammation are obviously intertwined. But we have a new way to find people with heart attacks liability.

I mean, a 30-fold risk. How many things in our world in medicine where we have a 30-fold risk? You know, it's like, wow. So it's exciting to see this. We don't have it in the U.S. You know, we have ways to do CT angiograms, but no AI package that's available to do the inflammation index. the data here is really impressive.

To connect the stories of organ clocks and heart inflammation, it seems like a theme of this generation of breakthroughs is that we're getting a lot smarter about detection, right? I mean, that's what we're talking about with being able to sample the protein levels in your blood to determine the age of your organ. That's detection. being able to run a test that determines whether or not your inflammation is 30x normal and therefore likely to a heart attack or some kind of cardiovascular event. Another example of detection. Is there a big picture reason why we seem to be in this golden age of disease detection?

Yeah, well, I think it opened up when we started being able to assay 11,000 proteins in our blood, you know, with a tiny blood sample. There's even now, Derek, a newly reported sensor you can put in the body that would monitor inflammation in your body from proteins continuously. So as opposed to this very primitive CRP blood test, we're now going to have either a plasma protein panel or a sensor. And the point here is that the diseases, the big three diseases, whether it's brain degenerative disease or cancer or cardiovascular.

They have one thing in common, which is hyper inflammation, often below the surface without symptoms. So if we know people are, whether it's an inflamed coronary or an inflamed brain, we have sensors, whether it's, you know, taking a blood test or potentially someday the report in science of this continuous real-time sensor of inflammation proteins. What the common theme is, we just didn't have this window into our body's proteins until now, whether it's thousands of proteins or sensor or panels, it's happening really quickly. So we paid so much hummage to the genome revolution, starting, you know, back in 2000 with the human genome sequence. But if you look at what's really happening today, the protein revolution is taking hold at a pace that's just dizzy, really.

And that's what I really want to understand better, is why do we have such a brilliantly clear window into not only our protein levels, but what those protein levels mean for our health now? Like what are the, it could be one or two or a convergence of breakthroughs in AI or proteomics that allows us to read our proteins in a way that's just much more legible than it was 10, 20 years ago. Yeah, I mean, the most extraordinary example I've seen in recent times was when not only could you get proteins in the blood, but you can get proteins of the cell. So that was the work, you know, done in Germany where they did laser capture of the cells and got the hundreds of proteins in the cell,

cracked the case for why these people would have died with toxic epidermal necrolysis, and then found the pathway that was causing these sick proteins to form, and they saved their lives. I mean, so again, the protein story has been just to see something like that, you know, I call it, you know, spatial medicine, but, you know, we talked about spatial biology, spatial omics, but the protein story is just unraveling things like that. I mean, when you it starts to become life-saving, then you start to realize, hmm, you know, that doesn't require a lot of proof that it's doing something good. So I think it's just a matter of an outpouring of data at a body-wide level, at a tissue level, organ level, and even cellular level. This is something

that's extraordinary, and it's all happening in this, you know, in this very dense, you know, at one time, a cluster, if you will. Eric, final question. I've read that a lot of the advances we've made in proteins and understanding biomarkers come from the UK Biobank and similar projects that inventory the health data of thousands and thousands of people. Can you tell us what is the UK Biobank and how are its discoveries and the discoveries of other biobanks connected to these breakthroughs we've been talking about today?

The UK Biobank, just to be clear for everyone, has 550,000 participants now have been followed for about 15 years. And a lot of the studies that I've been talking about today with you are derived from that incredible resource, right? And it's publishing almost every day, sometimes, you know, more than, because the whole world research community is cracked into it. It's open for research. Now, all of us is about almost 800,000 people of diverse. ancestry, which is a good thing, but it only has a much more limited follow-up since it's new. But the other thing is, of course, the UK is now, it wasn't happy just with the UK Biobank. Now they have our future health.

Five million people, already 2 million people, participants within a year. So we're getting these massive data banks with multi-year follow. And that's where the things that we've been talking about today are getting their validation, because it's already assembled. The samples are just stored. You can just check and see, oh, well, this protein, how did it correlate with, and we have their genomes, and we have all their electronic records. So when you have all these layers of data and you have people already 15 years of follow,

that's when you can say, hey, this stuff really works. This stuff is really meaningful. And so it gives us a jump. And one other thing I just want to mention with this organ clock story in these proteins, You know there's a lot of candidate drugs maybe someday for promoting health span and longevity. But the FDA, there's no path to get approval because we don't consider aging a disease, right? But what we don't know and what is tantalizing, what if you had this drug that significantly, substantially slowed the aging of a organ clock? you know, by many years in a person who was, you know, susceptible to that, would that be

enough criteria to approve that using a protein panel and a drug? So that's where this could be headed someday. It changes the potential ground rules for a drug that is designed to change aging, not at a body-wide level, but at a particular organ or individual level. It's fascinating. Eric Tobel, thank you. so much. Thank you, Derek. It's been fun. Thank you for listening. Plain English is hosted, written, and researched by me, Derek Thompson, produced by Devin Beraldi. In 2025, we are coming back to you with our regular schedule of two-ish episodes per week. We've got some awesome features cooking. We're very excited to share them with you. Thanks for listening, as always.

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