Speaker 1: Welcome to the Sentient Code, where intelligence is engineered, autonomy
Speaker 1: is emerging, and a line between human and machine grows thinner.
Speaker 1: Each episode, we decode the algorithms, explore the robotics, and
Speaker 1: examine the ideas shaping the future of artificial minds.
Speaker 2: Welcome back today we are we're tackling a subject that
Speaker 2: honestly feels a little bit like we're stepping into the
Speaker 2: middle of a massive theoretical physics debate.
Speaker 3: Yeah, it really does.
Speaker 2: But the twist here is that it's actually happening right now,
Speaker 2: Like in engineering labs all over the world.
Speaker 3: It is. And it's one of those topics where if
Speaker 3: you just pay attention to the headlines, you might think
Speaker 3: you know the whole story. You see the press releases
Speaker 3: and the hype cycles, But the real story, the actual
Speaker 3: engineering reality of it all, is hiding deep in the details.
Speaker 2: Exactly so for everyone listening, let's set the scene for
Speaker 2: this deep dive. If you were to ask someone to
Speaker 2: name the two most you know, buzzworthy, world changing technologies
Speaker 2: of the twenty first century, I think pretty much everyone
Speaker 2: would give you the same two.
Speaker 3: Answers, Artificial intelligence and quantum computing.
Speaker 2: Right, AI and quantum and usually when we think about them,
Speaker 2: we picture them as these entirely parallel tracks.
Speaker 3: Two totally separate disciplines.
Speaker 2: Yeah, exactly that. You know, over here in this one
Speaker 2: building you have the AI researchers building these massive, large
Speaker 2: language models. They're obsessing overweights and biases and neural nets. Sure,
Speaker 2: and then way over there, maybe in some super cooled
Speaker 2: underground lab, you have the physicists and they're just trying
Speaker 2: to isolate a quibit. They feel like entirely separate worlds.
Speaker 3: That is definitely the common perception, right, It's almost like
Speaker 3: two different genres of science, right. One is all about software, right,
Speaker 3: pattern recognition, statistics, massive data sets, and the other is
Speaker 3: deeply rooted in hardware, coherent states and fundamental physics. But
Speaker 3: what we found in the en Else's for today is
Speaker 3: that this separation is actually a huge misconception.
Speaker 2: That was a big aha moment for me reading through
Speaker 2: these sources. They aren't just parallel lines. They are quite
Speaker 2: literally entangled.
Speaker 3: They are growing up together. Yeah, and it's not just
Speaker 3: that they happen to exist at the same time in history.
Speaker 3: The relationship is much more intimate than that.
Speaker 2: They're accelerating each.
Speaker 3: Other precisely, they are accelerating each other. In fact, the
Speaker 3: sources we're looking at make a pretty strong case that
Speaker 3: they are becoming intimately dependent on one another.
Speaker 2: So that's our mission today for you guys listening. We
Speaker 2: are going to explore this reciprocal partnership. We really want
Speaker 2: to understand how AI is essentially acting as the mechanic
Speaker 2: that is building quantum computers right now, yes, and then
Speaker 2: we're going to flip the script and look at how
Speaker 2: quantum computers might eventually completely revolutionize AI.
Speaker 3: It's a fascinating story of a feedback loop. But before
Speaker 3: we get into that feedback loop, we have to understand
Speaker 3: the players, right.
Speaker 2: We need to set the stage exactly.
Speaker 3: We need to set the stage with the fun fundamental
Speaker 3: problem that both of these technologies are desperately trying to solve.
Speaker 3: And I really don't want to start with this standard
Speaker 3: what is a bit stuff?
Speaker 2: Oh?
Speaker 3: Please know right, our listeners know what a binary system is.
Speaker 2: Yeah, we definitely don't need to do the whole imaginal
Speaker 2: light switch analogy. Let's talk about complexity. Let's talk about
Speaker 2: the classical wall.
Speaker 3: The classical wall. I love that term because this isn't
Speaker 3: just about speed.
Speaker 2: It's not about making your laptop faster.
Speaker 3: No, it's not about how fast you can render a
Speaker 3: four K video or query a massive SQL database. Classical computers,
Speaker 3: whether it's your phone or a massive supercomputer like Frontier,
Speaker 3: they operate within a very specific complexity class.
Speaker 2: And this brings us right to P versus NP, right,
Speaker 2: and specifically the simulation of nature itself.
Speaker 3: That is the absolute crux of it. The sources highlight
Speaker 3: over and over that classical computers fundamentally struggle with problems
Speaker 3: that involve massive combinatorial spaces.
Speaker 2: And the specific example that keeps coming up in the
Speaker 2: literature is simulating quantum systems, like trying to model the
Speaker 2: Hamiltonian of a molecule.
Speaker 3: Right, And just to clarify, the Hamiltonian being the operator
Speaker 3: corresponding to the total energy of that system exactly.
Speaker 2: So, if you want to simulate how a molecule behaves,
Speaker 2: let's say you're trying to design a new drug or
Speaker 2: maybe a better catalyst for carbon capture, you have to
Speaker 2: track the interactions of all its electrons.
Speaker 3: But electrons are quantum objects. They exist in superposition, they're
Speaker 3: entangled with one another.
Speaker 2: And the math there gets brutal.
Speaker 3: So fast it's exponential. It is brutally exponentially. If you
Speaker 3: have a system with n electrons the state space, you
Speaker 3: need to simulate scales as two to the power of en.
Speaker 2: So, just to put that in perspective, if I add
Speaker 2: just one single electron to my simulation, I literally double
Speaker 2: the amount of memory required.
Speaker 3: You double it every single time, and that gets entirely
Speaker 3: out of hand almost instantly. To simulate a relatively simple
Speaker 3: molecule like say, caffeine, with perfect quantum fidelity on a
Speaker 3: classical machine, you would need a computer memory larger than
Speaker 3: the number of atoms in the observable universe.
Speaker 2: Which is just wild to think about. That is a
Speaker 2: hard limit. That's not something Moore's law is ever going
Speaker 2: to solve for us. You can't just throw more GPUs
Speaker 2: at that problem and hope for the best.
Speaker 3: You physically cannot build a classical computer big enough, right period.
Speaker 3: That's the classical wall. And that is exactly why Richard Feynman,
Speaker 3: way back in the nineteen eighties said that if you
Speaker 3: want to simulate nature, you'd better make it a quantum
Speaker 3: mechanical simulation.
Speaker 2: Enter the quibbot and again let's skip the basic it's
Speaker 2: a coin spinning in the air analogy. Because the real
Speaker 2: power here isn't just the superposition itself, right, it's the
Speaker 2: state space it opens up.
Speaker 3: It's the ability to manipulate that enormous exponential state space directly.
Speaker 3: A quantum computer with roughly three hundred perfectly functioning quibods
Speaker 3: could represent more states simultaneously than there are atoms in
Speaker 3: the universe.
Speaker 2: It doesn't just simulate the physics, no, it essentially is
Speaker 2: the physics. And for very specific problems like factoring large
Speaker 2: integers for breaking cryptography, or searching map of unstructured databases,
Speaker 2: or simulating these complex Hamiltonians, the speed up isn't just
Speaker 2: like a ten x or one hundred x improvement.
Speaker 3: It's asymptotic. Yeah, it completely changes the math. It turns
Speaker 3: a practically impossible exponential problem into a solvable polynomial one.
Speaker 3: We are literally talking about the difference between finding an
Speaker 3: answer in a few minutes versus finding it in the
Speaker 3: age of the universe.
Speaker 2: Okay, So if the math is so clearly undeniably superior,
Speaker 2: why are we still recording this deep dive on traditional silicon?
Speaker 2: What is the catch here?
Speaker 3: The catch is what the industry calls the hardware crisis. Right,
Speaker 3: We can write the beautiful math on a chalkboard all
Speaker 3: day long, but building the physical machine is an absolute nightmare.
Speaker 3: Quibbits are incredibly fragile.
Speaker 2: And when we say fragile, we aren't talking about, you know,
Speaker 2: dropping the chip on the floor.
Speaker 3: No, we're talking about information fragility decoherence. To maintain that
Speaker 3: delicate state of superposition, the quibbit has to be perfectly
Speaker 3: isolated from the outside environment.
Speaker 2: Because the universe is fundamentally noisy, very.
Speaker 3: Noisy heat, electromagnetic fluctuations, vibration, even a stray cosmic ray,
Speaker 3: literally everything around it is trying to couple with that quibbit.
Speaker 2: And the moment the environment interacts.
Speaker 3: With it, the wave function collapses.
Speaker 2: The quantum information just leaks out into the environment exactly.
Speaker 3: That is decoherence. Currently, our absolute best superconducting quibots can
Speaker 3: only hold their state for milliseconds, sometimes just microseconds, before
Speaker 3: they just degrade into random noise.
Speaker 2: So we have a theoretical computer that is more powerful
Speaker 2: than the universe itself, but it essentially crashes every thousandth
Speaker 2: of a second.
Speaker 3: That is a very fair summary of where we are
Speaker 3: right now. This is the NIIC Q era, the noisy
Speaker 3: intermediate scale quantum era. The hardware is just noisy.
Speaker 2: And this brings us to the first half of our
Speaker 2: partnership because this extreme fragility is exactly where artificial intelligence
Speaker 2: steps into the picture. Yes, this is the part of
Speaker 2: the sources I found so fascinating. We usually think of
Speaker 2: AI as the software thing that runs on the computer
Speaker 2: once it's built, but here the literature describes AI as
Speaker 2: the crucial tool used to actually build and fix the
Speaker 2: computer itself.
Speaker 3: It acts as the mechanic. Building a quantum computer requires
Speaker 3: solving an array of control problems that are individually enormously complex.
Speaker 3: You have to calibrate these tiny devices, route the microwave signals,
Speaker 3: perfectly correct errors on the fly. Human engineers simply cannot
Speaker 3: keep up with it.
Speaker 2: Let's look at calibration first, because the sources spend a
Speaker 2: lot of time on this. They mentioned that a quantum
Speaker 2: processor isn't just a plug and play device. You don't
Speaker 2: just hit a power button.
Speaker 3: Far from it. Imagine you have a superconducting processor. Let's
Speaker 3: say it has one hundred and twenty seven quibits. Each
Speaker 3: one of those individual quibits has its own unique resonant frequency,
Speaker 3: It has a specific in harmonicity, it has a highly
Speaker 3: specific coupling strength to the quibbots sitting right next to it.
Speaker 2: And the frustrating part is those properties aren't static.
Speaker 3: They constantly drift. They are notoriously unstable. As the system ages,
Speaker 3: or even if the temperature and the massive dilution refrigerator
Speaker 3: fluctuates by just a fraction of a millikelvin, those parameters shift.
Speaker 2: It's like trying to play a grand piano where the
Speaker 2: strings are just constantly loosening and tightening on their own
Speaker 2: while you're trying to play a concerto.
Speaker 3: That is a perfect analogy. You have to retune it constantly.
Speaker 3: And the old method for doing this was manual calibration
Speaker 3: or using very traditional linear graph based models.
Speaker 2: Which means you'd have to stop everything you're doing.
Speaker 3: Right, You stop the computation and you run a long
Speaker 3: series of traditional Ramsey experiments just to figure out what
Speaker 3: the new frequency of each quibit is.
Speaker 2: And that takes a lot of time.
Speaker 3: It takes hours. You're looking at hours of downtime just
Speaker 3: to get maybe a few minutes of actual reliable compute time.
Speaker 3: It's wildly inefficient.
Speaker 2: So how exactly does AI change that equation? How does
Speaker 2: it fix the tuning problem?
Speaker 3: Engineering teams are now using machine learning models, very often
Speaker 3: graph neural networks to predict these drifts before they ruin
Speaker 3: the calculation. The AI continuously monitors the system's background diagnostics.
Speaker 2: So it's watching the temperature or the ambient noise.
Speaker 3: Yes, it sees a tiny temperature fluctuate, and it thinks ah.
Speaker 3: Based on the last ten thousand hours of operation data,
Speaker 3: I know that quibit forty three is about to drict
Speaker 3: by two megahotes.
Speaker 2: So it predicts the drift proactively exactly.
Speaker 3: You can identify the anomalous quivots, the ones that are
Speaker 3: starting to act up instantly. This reduces calibration time from
Speaker 3: hours down to just minutes. It keeps the machine operating
Speaker 3: in that sweet spot much much longer.
Speaker 2: Okay, so AI is acting as this active real time stabilizer.
Speaker 2: But let's go a layer deeper, because the real bottleneck,
Speaker 2: according to all the papers were reviewed, is error correction
Speaker 2: fault tolerance.
Speaker 3: That is the holy grail of quantum computing. We fundamentally
Speaker 3: need to be able to fix errors faster than they
Speaker 3: naturally occur, and.
Speaker 2: This is where the geometry of it all gets really interesting.
Speaker 2: The sources talk extensively about something called the surface code.
Speaker 3: The surface code is currently the leading architecture for achieving this.
Speaker 3: The core idea is to create what we call a
Speaker 3: logical quibit, which is.
Speaker 2: The actual data bit you care about.
Speaker 3: Right, But because physical equibits are so fragile, you don't
Speaker 3: store that logical bit on one single physical wire. You
Speaker 3: smear the information across a massive checkerboard pattern of many
Speaker 3: many physical quibbitts. Safety in numbers, pure redundancy. But here's
Speaker 3: the trick, and it's a deeply counterintuitive one. If you're
Speaker 3: used to classical computing, you cannot just look at the
Speaker 3: quibbitts to check if an error happened.
Speaker 2: Because if you measure the data equibits.
Speaker 3: You destroy the entanglement exactly, you collapse the superposition and
Speaker 3: you instantly lose the entire calculation.
Speaker 2: So if you can't look at them, how do you
Speaker 2: know if an error occurred?
Speaker 3: You use what are called ancill equipments. Think of them
Speaker 3: as help equibits. You interleave them with your data equibits,
Speaker 3: and you carefully entangle these helpers with the data equibits
Speaker 3: to perform a parity check.
Speaker 2: A parity check. Let's break that down.
Speaker 3: So you aren't asking the data equibit are you currently
Speaker 3: zero or one? You are asking a small group of them.
Speaker 3: Did any of you suddenly relative to your neighbors got it?
Speaker 2: So if they usually agree and suddenly the math shows
Speaker 2: they disagree, the Helper and Sill equibit lights up.
Speaker 3: Exactly, and that pattern of Ancilla measurements the ones that
Speaker 3: loy up is called the syndrome.
Speaker 2: The syndrome, it's like the shadow of the error rather
Speaker 2: than the error itself.
Speaker 3: It's a great way to think of it. It's the
Speaker 3: diagnostic symptom.
Speaker 2: Okay, so you have this continuous stream of syndrome data
Speaker 2: coming out of the fridge. Why do we need an
Speaker 2: advanced AI for that? Can't a classical computer just look
Speaker 2: up the error pattern in a pre computed table and
Speaker 2: apply effects.
Speaker 3: In an idealized, purely theoretical world. Yes, you use a
Speaker 3: standard algorithm. The most famous one is called minimum weight
Speaker 3: perfect matching. It basically looks at the syndrome grap and
Speaker 3: finds the simplest, shortest set of errors that explains the
Speaker 3: lights turning.
Speaker 2: On Oakham's razor. The simplest explanation is usually the right
Speaker 2: one precisely.
Speaker 3: But here is the massive rub. The physical hardware is
Speaker 3: not ideal, it's messy. The source is heavily emphasize the
Speaker 3: problem of correlated noise.
Speaker 2: What does correlated noise actually look like in a quantum chip.
Speaker 3: It means errors don't happen in isolation. If quibbit A
Speaker 3: suffers an error, it physically affects quibbitt B right next
Speaker 3: to it. Maybe a high energy cosmic ray hits the
Speaker 3: silicon substrate and wipes out a whole localized patch of
Speaker 3: quibbits at once. Or there's cross stock right crosstock between
Speaker 3: the microwave control lines. The errors cluster together in weird,
Speaker 3: highly non random ways.
Speaker 2: And the standard algorithms, those traditional graph matters you mentioned,
Speaker 2: they assume errors are completely independent.
Speaker 3: Usually yes, they assume a simple noise model, so when
Speaker 3: they encounter complex correlated noise in the real world, they
Speaker 3: get completely confused. They essentially hallucinate the wrong correction.
Speaker 2: And if you apply the wrong correction to a quantum state, you.
Speaker 3: Actively inject more errors into the system. You kill the
Speaker 3: logical quibot yourself.
Speaker 2: But a neural network a.
Speaker 3: Neural network absolutely loves correlations. That is quite literally what
Speaker 3: deep learning is built to find. You can train a
Speaker 3: neural network on the highly specific, messy noise fingerprint of
Speaker 3: one individual chip. It learns through observation that oh on
Speaker 3: this specific processor, when Quibot five flips, Quibot six almost
Speaker 3: always rotates by a few degrees.
Speaker 2: It learns the unique personality of the hardware.
Speaker 3: It really does, and the empirical data shows that these
Speaker 3: neural network decoders can interpret these complex syndromes much faster
Speaker 3: and far more accurately than the standard algorithms. It's a
Speaker 3: difference between a general practitioner doctor who just strictly follows
Speaker 3: a textbook checklist and a season specialist who has seen
Speaker 3: this highly specific, rare disease pattern a thousand times in
Speaker 3: the clinic.
Speaker 2: That is a massive leap forward. It's taking us from
Speaker 2: purely theoretical error correction on paper to practical hardware aware
Speaker 2: error correction in the real world.
Speaker 3: It's the bridge that makes fault tolerance actually achievable.
Speaker 2: Let's move up the stack a little bit. We have
Speaker 2: AI calibrating the machine. We have AI correcting the localized errors.
Speaker 2: Now we actually have to run a program. We have
Speaker 2: to run code. This brings us to the section the
Speaker 2: sources call optimization and compilation.
Speaker 3: Yes, the tetris phase of.
Speaker 2: Quantum computing, but incredibly high stakes.
Speaker 3: Tetris extremely high stakes. You see, when a programmer writes
Speaker 3: a quantum algorithm, they write it in a very abstract way.
Speaker 3: They might write a line of code that says, apply
Speaker 3: an entangling c and Ot gate between quibut one and
Speaker 3: quibit ten.
Speaker 2: But on the actual physical silicon chip down in the fridge,
Speaker 2: quibuit one might be nowhere near.
Speaker 3: Quibut ten exactly. Physical chips have a specific topology. They
Speaker 3: have a layout. Maybe it's a square grid, or maybe
Speaker 3: it's a heavy X lattice, which is what IBM tens
Speaker 3: to use in these layouts. Physical qubits can only talk
Speaker 3: directly to their immediate physical neighbors.
Speaker 2: So if I want to entangle quibut one and quibit
Speaker 2: ten and they aren't physically touching, you.
Speaker 3: Have to manually move the quantum information across the chip.
Speaker 3: You have to use what are called swapgates. You swap
Speaker 3: the quantum state of quibit one into quibit two, and
Speaker 3: then from two to three and so on, cascading it
Speaker 3: until it is physically sitting right next to quibut ten.
Speaker 2: But every single swapgate takes physical time to execute.
Speaker 3: Time is the enemy. Every nanosecond adds exposure to noise.
Speaker 3: Every extra gate you add lowers the overall fidelity of
Speaker 3: your entire circuit. So you end up with a massive
Speaker 3: complex optimization problem. How do I map this highly abstract
Speaker 3: software circuit onto this rigid physical graph using the absolute
Speaker 3: fewest possible swapgates.
Speaker 2: That sounds suspiciously like the traveling salesman problem.
Speaker 3: It essentially is. It is an NP hard routing problem
Speaker 3: for large complex quantum circuits. Finding the mathematically perfect mapping
Speaker 3: is computationally impossible. For traditional classical solvers, they just choke
Speaker 3: on the complexity they take too long.
Speaker 2: So enter the AI architect reinforcement learning.
Speaker 3: Specifically, researchers are deploying agents very similar to alpha zero.
Speaker 2: You mean the AI that famously mastered the games of
Speaker 2: Go and chess.
Speaker 3: The very same architecture.
Speaker 2: How does that apply to routing quantum circuits.
Speaker 3: Well, think of the quibbit topology. The layout of the
Speaker 3: chip as the board. The quantum states are the pieces,
Speaker 3: the plays the game of routing the circuit millions and
Speaker 3: millions of times. In simulation, it gets a reward signal
Speaker 3: for minimizing the overall circuit depth, and it gets heavy
Speaker 3: penalties for adding unnecessary swap gates.
Speaker 2: And through playing this game, it discovers routing strategies that
Speaker 2: human engineers miss.
Speaker 3: It absolutely does. It consistently finds non intuitive, brilliant paths.
Speaker 3: It recognizes deep patterns in the circuit structure. It might
Speaker 3: look at the code and realize, oh, this specific block
Speaker 3: of operations mathematically resembles a quantum foury eight transform, so
Speaker 3: I can fold it this particular way on the heavy
Speaker 3: hex lattice to save twenty gates. It actively squeezes every
Speaker 3: drop of efficiency out of the imperfect hardware in ways
Speaker 3: static compilers just cannot match.
Speaker 2: So, just to recap this whole first half of the
Speaker 2: discussion for you listening, we have AI tuning the physical instrument.
Speaker 2: We have AI actively diagnosing and fixing the errors, and
Speaker 2: we have AI rewriting the sheet music so it perfectly
Speaker 2: fits the quarks of the instrument.
Speaker 3: That is a perfect summary. AI is the critical infrastructure
Speaker 3: layer that is making quantum computing plausible. Without it, the
Speaker 3: sheer crushing complexity of managing the system entirely overwhelms us.
Speaker 2: Okay, so now I want to flip the script entirely.
Speaker 2: We've solidly established that quantum computing absolutely needs AI to function,
Speaker 2: But does AI actually need quantum This is.
Speaker 3: Where we have to tread very carefully. We are entering
Speaker 3: the realm of quantum machine learning or QML, and I
Speaker 3: really want to be clear upfront, there is an enormous
Speaker 3: amount of hype in this specific area that the sources
Speaker 3: explicitly caution us against, right.
Speaker 2: Because you see these wild headlines all the time, things
Speaker 2: like new quantum computer will train GPT five in three.
Speaker 3: Seconds, and that is at least currently pure science fiction.
Speaker 3: The expert analysis in all of our sources is actually
Speaker 3: quite skeptical of those broad sweeping claims.
Speaker 2: Let's unpack the theoretical advantage, though. Why do people even
Speaker 2: think a quantum computer would help AI in the first place.
Speaker 3: It all fundamentally comes down to linear algebra. Modern AI
Speaker 3: at its absolute core is just mass matrix multiplication. It's
Speaker 3: navigating high dimensional vector spaces.
Speaker 2: And quantum mechanics is also at its core linear algebra exactly.
Speaker 3: A quantum computer naturally manipulates vectors within a vast Hilbert space.
Speaker 3: There is a famous theoretical algorithm, the HHL algorithm, that
Speaker 3: proves a quantum computer can solve massive systems of linear
Speaker 3: equations exponentially faster than any classical computer ever could.
Speaker 2: So the industry logic basically goes AI is linear algebra,
Speaker 2: Quantum is extremely fast at linear algebra. Therefore quantum will
Speaker 2: supercharge AI.
Speaker 3: That's the compelling syllogism. Yes, but there are major caveats
Speaker 3: hiding under the surface. The biggest one, the sources point out,
Speaker 3: is what we call the input problem.
Speaker 2: You mean actually loading the data into the machine.
Speaker 3: Yes, we do not currently have quantum RAM. If you
Speaker 3: have a massive classical data set, like say the text
Speaker 3: of the entire Internet, which is what they use to
Speaker 3: train a large language model, loading all of that classical
Speaker 3: data into a coherent quantum state takes so unbelievably long
Speaker 3: that it completely wipes out any speed advantage you might
Speaker 3: get from the fast calculation itself.
Speaker 2: So the actual processing part is basically instant, but the
Speaker 2: loading dock is just a single file line exactly.
Speaker 3: It's a massive bottleneck. And then there is also the
Speaker 3: fascinating dequantization phenomenon.
Speaker 2: This part sounded like a literal plot twist in the
Speaker 2: academic research community.
Speaker 3: It really was. A few years ago, a brilliant young
Speaker 3: researcher named Ewen Tang was looking at a specific recommendation
Speaker 3: system algorithm, and this algorithm was widely believed to be
Speaker 3: exponentially faster on a quantum computer. It was a flagship
Speaker 3: example of quantum advantage in machine learning.
Speaker 2: And what did she find.
Speaker 3: She mathematically proved that if you make similar fair assumptions
Speaker 3: about how data is accessed in memory, you can actually
Speaker 3: run a very similar algorithm on a standard classical computer
Speaker 3: just as fast.
Speaker 2: Wow. So the supposed quantum advantage just evaporated.
Speaker 3: Completely in that specific case, Yes, it did. It turned
Speaker 3: out the massive speed up wasn't actually due to the
Speaker 3: magic of quantum mechanics at all. It was simply due
Speaker 3: to a very clever algorithmic structure that no one could
Speaker 3: bother to try on a classical machine. Yet, so, because
Speaker 3: of things like that, we have to be extremely skeptical
Speaker 3: of general exponential speed up claims for things like natural
Speaker 3: language processing or image recognition.
Speaker 2: However, there is one major area where the sources say
Speaker 2: the advantage is very real, it's robust, it's proven, and
Speaker 2: it's not about running chat GPT faster.
Speaker 3: No, it goes all the way back to what we
Speaker 3: discussed at the very start of the deep dive. The
Speaker 3: true killer app for quantum computing is molecular simulation, chemistry, chemistry,
Speaker 3: and advanced material science. This leads us to what is
Speaker 3: called the hybrid loop.
Speaker 2: Walk us through this loop. How exactly does this help AI?
Speaker 3: Well, imagine you are training an AI system to discover
Speaker 3: a revolutionary new solid state battery material. An AI model
Speaker 3: is fundamentally only as good as its training data. We
Speaker 3: all know the saying garbage in, garbage out.
Speaker 2: And right now, where do we get the data on
Speaker 2: how molecules interact at a quantum level.
Speaker 3: You get it from physical wet lab experiments which are
Speaker 3: incredibly slow and incredibly expensive. Or we get it from
Speaker 3: classical computer simulations, which are always approximations because they hit
Speaker 3: that classical wall we talked about. So our current training
Speaker 3: data is either very scarce or its low fidelity.
Speaker 2: So the quantum computer steps in as the ultimate data generator.
Speaker 3: Precisely, you use the quantum computer to simulate the absolute
Speaker 3: ground truth. Quantum states of these novel molecules. It generates
Speaker 3: impossibly high fidelity, perfect data that classical computers physically cannot calculate.
Speaker 3: And then then you feed that perfect, rich data into
Speaker 3: a classical neural network. The AI learns from the pristine
Speaker 3: quantum truth.
Speaker 2: I love this framing. So the quantum computer isn't trying
Speaker 2: to be the brain. It acts as the eyes. It
Speaker 2: directly sees the complex truth of nature, and the classical
Speaker 2: AI is the brain that actually processes and learns from it.
Speaker 3: That is a beautiful, very accurate way to put it.
Speaker 3: It's not about quantum replacing AI, not at all. It's
Speaker 3: about quantum providing the incredibly rich training data that classical
Speaker 3: AI currently lacks to solve physical problems. This hybrid loop
Speaker 3: could be the key to unlocking highly targeted drugs, room
Speaker 3: temperature superconductors, completely new catalysts for nitrogen fixation.
Speaker 2: That is where the real revolution happens. It's not about
Speaker 2: getting a slightly faster chatbot. It's about fundamentally understanding and
Speaker 2: engineering the physical.
Speaker 3: World around us exactly. It's world altering now.
Speaker 2: Obviously, we are currently stuck in this noisy and ICQ era.
Speaker 2: We can't run these massive perfect molecular simulations just yet,
Speaker 2: because the hardware isn't fault tolerant. So how do we
Speaker 2: make do right now? The source has talked a lot
Speaker 2: about vqas variational condum algorithms AH vqas.
Speaker 3: This is the absolute workhourse of the current noisy era,
Speaker 3: and it is, by its very definition, a hybrid AI
Speaker 3: quantum collaboration.
Speaker 2: How does a VQA actually work? In practice?
Speaker 3: Think of it like tuning a radio dial, But mathematically
Speaker 3: you start with what's called a parameterized quantum circuit. Is
Speaker 3: a very short, shallow quantum program, and it has some
Speaker 3: knobs you can actively turn. Specifically, these are rotation angles
Speaker 3: on the quantum gates.
Speaker 2: Okay, so you have this short circuit with adjustable knobs.
Speaker 2: You run it.
Speaker 3: You run it, and you measure the final energy of
Speaker 3: the quantum system. You get a single number out. Then
Speaker 3: you immediately send that number over to a standard classical computer.
Speaker 2: And the classical computer acts as the supervisor the optimizer.
Speaker 3: Right, classical computer takes that number, uses a standard machine
Speaker 3: learning algorithm like gradient descent, and says, okay, that energy
Speaker 3: was a bit too high. Let's tweak angle theta one
Speaker 3: by two. Degrees in angle theta two by one degree
Speaker 3: and try again.
Speaker 2: So it's a very tight loop. Quantum measures the state,
Speaker 2: classical optimizes the parameters exactly.
Speaker 3: Quantum measures classical optimized. They loop back and forth thousands
Speaker 3: of times until they systematically walk down the mathematical hill
Speaker 3: and find the lowest possible energy state, which is the
Speaker 3: answer you're looking for.
Speaker 2: But there is a major mathematical problem hiding in here too.
Speaker 2: The source is called it the barren plateau.
Speaker 3: Yes, this is a fascinating, deeply frustrating problem. It's essentially
Speaker 3: a geometry problem operating an incredibly high dimensional space.
Speaker 2: I mean, a barren plateau sounds like a really bad
Speaker 2: place to be stranded.
Speaker 3: It is the worst place to be stranded. If you're
Speaker 3: an algorithm, imagine you are literally trying to find the
Speaker 3: very bottom of a deep valley in a vast mountain range.
Speaker 3: Usually you look at the slope of the ground under
Speaker 3: your feet, the gradient, and you just keep walking downhill.
Speaker 2: Simple enough, that's how gradient descent works.
Speaker 3: Right, But in these massive quantum landscapes, as you add
Speaker 3: more and more quibits to your circuit, the mathematical landscape
Speaker 3: becomes exponentially vast and almost entirely flat. You're suddenly standing
Speaker 3: on a featureless plateau that extends infinitely in every direction
Speaker 3: the slope. The gradient is exactly zero everywhere, so.
Speaker 2: You literally don't know which way is downhill. You're completely
Speaker 2: lost in the fog.
Speaker 3: The gradients vanish exponentially. That is the barren plateau problem.
Speaker 3: The classical optimizer gets complete stuck because it receives no
Speaker 3: signal whatsoever on which direction to turn the knobs.
Speaker 2: And this is where AI comes writing to the rescue
Speaker 2: yet again.
Speaker 3: It is researchers are taking advanced techniques strom out of
Speaker 3: deep learning, things like transfer learning and recurrent neural networks
Speaker 3: rn ns, and using them to intelligently guess the initial
Speaker 3: starting parameters.
Speaker 2: So instead of just starting in a totally random spot
Speaker 2: on the flat plateau and hoping for the best.
Speaker 3: The AI analyzes the fundamental structure of the problem before
Speaker 3: it even starts and says, don't start in the middle.
Speaker 3: Start way over here, right near the edge of the cliff,
Speaker 3: where there's actually a slope. It gives the VQA a
Speaker 3: highly educated warm start.
Speaker 2: It's honestly amazing how these concepts from deep learning. You know,
Speaker 2: gradients back propagation. Vanishing gradients are sharing the exact same
Speaker 2: mathematical framework as these cutting edge quantum algorithms.
Speaker 3: They are mathematical cousins. If you think about it, a
Speaker 3: parameterized quantum circuit is basically just a neural network, but
Speaker 3: instead of digital neurons you have quivts, and instead of
Speaker 3: syneptic weights, you have rotation angles.
Speaker 2: That completely explains why the researchers are merging, why the
Speaker 2: physicists are suddenly having to become machine learning experts, and
Speaker 2: the mL engineers are studying quantum mechanics.
Speaker 3: The two fields are converging on the exact same underlying math.
Speaker 2: I want to pivot to something that I thought was
Speaker 2: one of the absolute weirdest parts of the source material.
Speaker 2: It's this black box mystery. Ah.
Speaker 3: Yes, using AI for quantum control at the physical layer.
Speaker 2: So earlier we talked about quantum gits being the software
Speaker 2: operations like lines of code, but physically down in the machine.
Speaker 2: A gait isn't a physical switch that flips. It's a
Speaker 2: pulse of energy.
Speaker 3: Right, It's an analog microwave or laser pulse. You literally
Speaker 3: blast the tiny physical equivot with a highly specific frequency
Speaker 3: for highly specific duration of time to gently rotate its
Speaker 3: state from a zero to a one.
Speaker 2: And you obviously want that microwave pulse to be perfect.
Speaker 2: You want it to be as fast as possible, but
Speaker 2: not so aggressive that it accidentally leaks energy into higher
Speaker 2: unwanted states.
Speaker 3: Exactly, superconnecting transmon equibbets aren't just simple two level systems.
Speaker 3: They aren't just zero, one, one. There is a level two,
Speaker 3: level three. Those are called leakage states. If you hit
Speaker 3: the quibbit too hard or with a Messi waveform, you
Speaker 3: accidentally push the quibbit up the energy ladder into those
Speaker 3: higher states, and you completely ruin the computation.
Speaker 2: Now, traditional physics uses elegant mathematical equations basically solving Schrodinger's
Speaker 2: equation to meticulously design the shape of these microwave pulses.
Speaker 2: They end up being these very smooth, elegant, mathematically pure shapes.
Speaker 3: The standard physics based algorithm for that is called GRAPE,
Speaker 3: and it heavily relies on having a perfect mathematical model
Speaker 3: of the quantum system.
Speaker 2: But the sources say labs are now throwing that out
Speaker 2: and using reinforcement learning instead. They literally just let an
Speaker 2: AI agent play with the knobs on the waveform generator directly,
Speaker 2: and here.
Speaker 3: Is where we get the epistemological twist, the part that
Speaker 3: some physicists find genuinely spooky.
Speaker 2: I have to be completely honest, Reading this specific section
Speaker 2: gave me a bit of an existential pause. The RL agents,
Speaker 2: through trial and error, sometimes find pulse shapes that work
Speaker 2: significantly better than the pure, mathematically designed ones humans come
Speaker 2: up with. But the actual shape of the AI's pulse it.
Speaker 3: Looks like total garbage, right if you look at it
Speaker 3: on an ascilloscope. It's jagged, it's weirdly squiggly. It looks
Speaker 3: like random static noise.
Speaker 2: And the brilliant physicists look at this swiggly waveform and
Speaker 2: they say, I have absolutely no idea why that works.
Speaker 3: It wildly outperforms the elegant physics based design, but it
Speaker 3: completely defies human intuition and standard physical models.
Speaker 2: But doesn't that feel risky? I mean, if the lead
Speaker 2: physicist looks at the wave and says, I don't know
Speaker 2: why this works, are we still fundamentally doing science or
Speaker 2: are we just sort of blindly hacking nature?
Speaker 3: At this point, that is the exact tension the field
Speaker 3: is wrestling with you see. The physicist designing the smooth
Speaker 3: pulse always assumes that quivid is a mathematically idealized perfect system.
Speaker 3: But the AI isn't using a theoretical model. It is
Speaker 3: interacting directly with the real methy physical object.
Speaker 2: And the real object has microscopic dirt on it, It
Speaker 2: has manufacturing defects.
Speaker 3: Exactly, It has microscopic acoustic vibrations from the cryostat pump.
Speaker 3: It has tiny unmodeled crosstalk couplings with the quibot. Three
Speaker 3: rows over that the elegant math completely ignores, the AI,
Speaker 3: through millions of trials, finds a bizarre way to actually
Speaker 3: use those specific imperfections to its advantage.
Speaker 2: So the AI is actively exploiting the physical bugs in
Speaker 2: the hardware, things the physicist dismisses as just background noise.
Speaker 3: It's quite literally turning the noise into a feature. It
Speaker 3: is surfing the physical imperfections of the chip.
Speaker 2: Which is brilliant from an engineering standpoint. I completely grant
Speaker 2: you that, but it strongly implies that to build these
Speaker 2: incredibly advanced machines we have to willingly surrender our fundamental
Speaker 2: understanding of them. We have to trust a black box
Speaker 2: AI to build and operate a black box quantum computer.
Speaker 3: It really does raise a profound philosophical question about the
Speaker 3: future of engineering as a discipline. Is it enough for
Speaker 3: a highly complex machine to simply work reliably or do
Speaker 3: we as humans inherently need to understand how it works
Speaker 3: at the lowest level. As these systems scale up and
Speaker 3: get exponentially more complex, we might honestly have to settle
Speaker 3: for the former.
Speaker 2: That is a very humbling thought, a bit terrifying, but humbling.
Speaker 2: Let's move slightly beyond just computing for a second, because
Speaker 2: the quantum revolution isn't just about crunching numbers in a
Speaker 2: data center. It's also heavily about sensing the world.
Speaker 3: Quantum sensing. Yeah, this is actually one of the fields
Speaker 3: that is quietly delivering massive practical advantages right now.
Speaker 2: Today we're talking about things like the gravitational wave detectors
Speaker 2: or next generation atomic clocks.
Speaker 3: And magnetometry using incredibly sensitive setups like nitrogen vacancy centers
Speaker 3: and v centers and synthetic diamondlitises to detect unimaginably small
Speaker 3: magnetic fields.
Speaker 2: And this has direct applications in medical imaging right like
Speaker 2: brain scan.
Speaker 3: Yes, specifically meg magneto and cepholography. Using quantum sensors, you
Speaker 3: can actually measure the tiny, localized magnetic fields produced by
Speaker 3: individual neurons firing in the human brain. But again you
Speaker 3: run into the exact same massive problem noise. The magnetic
Speaker 3: signal from a single firing neuron is incredibly weak compared
Speaker 3: to the ambient magnetic noise of the hospital room, or
Speaker 3: even the baseline magnetic field of the Earth itself.
Speaker 2: It's trying to find the faintest possible signal buried under
Speaker 2: a mountain of.
Speaker 3: Noise, exactly, and once again, AI is being deployed to
Speaker 3: aggressively filter this data. You can train a deep neural
Speaker 3: network to meticulously separate the chaotic background noise from the
Speaker 3: incredibly faint quantum sensor's true signal.
Speaker 2: So combining these two tex streams means we could soon
Speaker 2: be looking deep underground for geological deposits or mapping the
Speaker 2: internal wiring of the human brain with a level of
Speaker 2: precision we've never had before.
Speaker 3: AI is essentially the digital lens that brings the blurry
Speaker 3: quantum picture into sharp focus. It acts as the ultimate filter,
Speaker 3: extracting the needle from the haystack.
Speaker 2: We also briefly touched on quantum generative models in the outline,
Speaker 2: and honestly this sounded a bit like the quantum version
Speaker 2: of chat GPT or mid journey.
Speaker 3: In a theoretical way. Yes, the specific concept the sources
Speaker 3: focus on involves what are called born machines.
Speaker 2: Borne machines, named after the physicist Max Bourne.
Speaker 3: Yes, exactly. The core idea is to use the raw
Speaker 3: quantum state itself to directly represent a highly complex probability distribution.
Speaker 2: But why go through the trouble of doing that on
Speaker 2: a quantum computer?
Speaker 3: Because certain probability distributions, especially those you find organically in
Speaker 3: nature or incredibly complex combinatorial optimization problems, are notoriously hard
Speaker 3: to represent efficiently with classical bits. They're just too high dimensional.
Speaker 2: We just keep coming back to that same fundamental bottleneck.
Speaker 2: The shear, richness and massive scale of the quantum state space.
Speaker 3: A single entangled quantum state can represent these incredibly common, complex,
Speaker 3: deeply correlated probabilities exponentially more compactly than any classical neural
Speaker 3: network ever could. So for generating truly novel molecular configurations
Speaker 3: for drug discovery, or for solving highly complex, multi variable
Speaker 3: financial risk models, A quantum barn machine might eventually become
Speaker 3: the ultimate generative AI.
Speaker 2: Okay, so we've covered a huge amount of ground here.
Speaker 2: We've looked at the AI mechanics, fixing the hardware, the
Speaker 2: massive applications in chemistry, and the slightly spooky science of
Speaker 2: black box control. But we have to look soberly at
Speaker 2: the long road ahead of us. We mentioned fault tolerance
Speaker 2: a lot earlier.
Speaker 3: Yes, the very long, very difficult road ahead.
Speaker 2: The sources gave some pretty sobering numbers here that I
Speaker 2: think we need to highlight. To do the truly world
Speaker 2: changing stuff, to break RSA encryption or to perfectly simulate
Speaker 2: a really large complex molecule, we absolutely need a fully
Speaker 2: fault tolerant quantum computer.
Speaker 3: Yes, the noisy NISQ chips we have today simply will
Speaker 3: not cut it for those mass algorithms. And get just
Speaker 3: one reliable, logical quibot, that one perfect fully error corrected,
Speaker 3: indestructible bit of data we talked about earlier. Using the
Speaker 3: standard surface code architecture, you realistically need roughly one thousand
Speaker 3: physical quibots one.
Speaker 2: Thousand to one. That is an incredibly steep exchange rate.
Speaker 3: It really is. So do the math if you need
Speaker 3: a quantum computer with say a few thousand logical quibots
Speaker 3: to do something genuinely useful like completely analyzing the nitrogenase enzyme.
Speaker 3: To revolutionize global fertilizer production, You're going to need millions
Speaker 3: of perfectly functioning physical quotas on a chip.
Speaker 2: And right now the biggest chips in the world have
Speaker 2: what a few hundred.
Speaker 3: We are squarely in the hundreds. Companies like IBM and
Speaker 3: Google are aggressively pushing toward the low thousands in the
Speaker 3: next few years. But we're talking about needing to scale
Speaker 3: up the physical hardware by multiple orders of magnitude.
Speaker 2: This isn't just a fun, little science experiment in a
Speaker 2: university basement anymore.
Speaker 3: Not at all. It is a massive, industrialized engineering feat
Speaker 3: that is comparable to the largest technological undertakings in human history.
Speaker 3: We're talking about a scale of engineering similar to building
Speaker 3: the large Hadron collider or launching the Apollo program.
Speaker 2: And the biggest takeaway from all the source material is
Speaker 2: that as we attempt to scale up to millions of quibits,
Speaker 2: AI isn't just acting as a helpful little sidekick. It
Speaker 2: is absolutely essential to the entire endeavor.
Speaker 3: It becomes utterly ubiquitous. Think about the sheer scale of
Speaker 3: the control problem. If you have a processor with one
Speaker 3: million fragile quibits, you physically cannot calibrate them by hand anymore.
Speaker 3: You cannot write the routing compiler by hand. You certainly
Speaker 3: cannot decode the millions of aero syndrome streaming out every
Speaker 3: microsecond by hand.
Speaker 2: You desperately need an incredibly fast, entirely automated intelligence layer
Speaker 2: just to keep the machine breathing.
Speaker 3: You need AI embedded at every single discrete layer of
Speaker 3: the technology stack. AI will design the microscopic physical layout
Speaker 3: of the silicon chip to actively minimize magnetic crosstalk. AI
Speaker 3: will tune the analog microwave control pulses in real time.
Speaker 3: AI will manage the massive complex error correction syndrome decoding
Speaker 3: without humans ever seeing it.
Speaker 2: It really feels like we are deliberately building a machine
Speaker 2: that is fundamentally too fast, too delicate, and too mathematically
Speaker 2: complex for the human brain to understand or operate. So
Speaker 2: we are being forced to use another machine, artificial intelligence,
Speaker 2: to build it, translate it, and run it for us.
Speaker 3: That is a very valid, incredibly profound perspective on where
Speaker 3: we are headed. We are basically building a technological ladder.
Speaker 3: We stand on the foundation of classical computing to build
Speaker 3: modern AI, and now we're actively using that AI to
Speaker 3: build quantum computing.
Speaker 2: So as we wrap up this deep dive, let's summarize
Speaker 2: the true nature of this partnership.
Speaker 3: For everyone listening, it's critically important to realize it's not
Speaker 3: a story of tool versus user. It's truly reciprocal. AI
Speaker 3: makes quantum computing physically plausible by fixing the hardware errors
Speaker 3: and managing the overwhelming noise, and in return, quantum computing
Speaker 3: will eventually make AI unimaginably powerful by providing entirely new
Speaker 3: computational substrates and most importantly, providing pristine new data straight
Speaker 3: from nature itself.
Speaker 2: And underneath it all. They fundamentally share language.
Speaker 3: Linear algebra, deep high dimensional vector spaces. The researchers are merging,
Speaker 3: the academic departments are merging. The two fields are slowly
Speaker 3: becoming one unified discipline.
Speaker 2: I really love the final provocative thought the source material
Speaker 2: left us with it directly challenges the very binary, categorized
Speaker 2: way we usually think about the future of technology.
Speaker 3: The idea that the future isn't binary. It's not going
Speaker 3: to be quantum or r classical. It isn't going to
Speaker 3: be purely silicon or are purely biological. It's going to
Speaker 3: be a weave, a massively complex hybrid weave of all
Speaker 3: of them working together, each technology playing exactly to its
Speaker 3: unique strengths. Will use classical computers for basic logic and
Speaker 3: data IO, will use advanced AI for complex pattern recognition
Speaker 3: and real time system control, and will use and processors
Speaker 3: exclusively for deep simulation and high complexity, combinatorial optimization.
Speaker 2: And the closing reflection for you to mull over, we
Speaker 2: are literally witnessing right now the story of how humanity
Speaker 2: finally learned to think collaboratively with machines.
Speaker 3: And perhaps more importantly, how those machines, driven by this
Speaker 3: incredibly tight quantum AI feedback loop, are rapidly learning to
Speaker 3: think and understand the universe in their own completely alien way.
Speaker 2: That is a lot of heavy stuff to process, but hey,
Speaker 2: that's exactly why we do these deep dives.
Speaker 3: Indeed it is. It's a fascinating time to be alive.
Speaker 2: Thank you for walking us through the entanglement of the century.
Speaker 2: It's been incredibly eye opening.
Speaker 3: My absolute pleasure. Thanks for having this quession, and to.
Speaker 2: All of you listening out there, Keep thinking, keep exploring
Speaker 2: the details, and we'll catch you on the next deep dive.