Speaker 1: Welcome to the deep dive. We're here to go cut
Speaker 1: through the noise and get you the insights that matter.
Speaker 1: And today we're tackling a really big one quantum computing.
Speaker 1: It's a term you hear a lot. Sounds very sci fi,
Speaker 1: very powerful, but honestly it can also feel pretty mysterious,
Speaker 1: kind of complex. So our mission for this deep dive
Speaker 1: is simple, pull back that curtain. We want to explore
Speaker 1: what quantum computing really is, what problems can it actually solve,
Speaker 1: and maybe most importantly from any of you, how do
Speaker 1: we even start programming these machines. We're leaning heavily on
Speaker 1: quantum programming and depth solving problems with Q sharking criskeet today.
Speaker 1: It gives nice practical angle.
Speaker 2: Absolutely, I'm looking at it now, is well, it's crucial.
Speaker 2: Understanding the basics helps us see the real potential, you know,
Speaker 2: help sort the genuine breakthroughs from just the hype. It's
Speaker 2: about where this is actually heading.
Speaker 1: Okay, let's jump right in that the big promise quantum computers.
Speaker 1: Are they going to replace our laptops, our phones? Is
Speaker 1: that the future we're talking about?
Speaker 2: Ah? Yeah, that's the classic question and probably the biggest misconception.
Speaker 2: The short answer is no, definitely not. Quantum computing isn't
Speaker 2: designed to take over everyday.
Speaker 1: Tasks, really not even faster.
Speaker 2: Nope. In fact, for things like email, web browsing, even
Speaker 2: a lot of standard data processing, a quantum computer would
Speaker 2: likely be much much slower slower.
Speaker 1: Okay, that's counterintuitive.
Speaker 2: It is they're just built differently. Their power isn't in
Speaker 2: general speed, it's entackling very specific, highly specialized problems. Think
Speaker 2: about problems where classical supercomputers, even the biggest ones, just
Speaker 2: hit a wall.
Speaker 1: Like what kinds of problems?
Speaker 2: Well, material science is a huge one. Simulating exactly how
Speaker 2: molecules behave at the quantum level, or simulating other quantum systems.
Speaker 2: These are problems where the underlying physics is quantum.
Speaker 1: So classical computers struggle because they're trying to imitate something
Speaker 1: fundamentally different.
Speaker 2: Precisely, the complexity just explodes for classical machines. But if
Speaker 2: we could simulate new materials accurately, think about the possibilities
Speaker 2: new catalysts for energy, better batteries, maybe room temperature superconductors,
Speaker 2: new drugs, it could genuinely revolutionize industries.
Speaker 1: Okay, so it's not a faster everything machine. It's more
Speaker 1: like a specialized scientific instrument, maybe a super microscope for
Speaker 1: certain kinds of problems.
Speaker 2: That's a great way to put it. A very powerful,
Speaker 2: very specific tool.
Speaker 1: So if it's this specialized tool, yeah, what does it
Speaker 1: actually look like and how do we interact with it?
Speaker 1: Are we talking glowing boxes? Well?
Speaker 2: The reality right now is interesting. We're in what John
Speaker 2: Preskill called the noisy intermediate scale quantum era NIQ for sure.
Speaker 1: A nice que Okay, what does that mean? In plain English?
Speaker 2: It means the devices we have now are big enough
Speaker 2: that simulating them perfectly on a classical computer is basically impossible.
Speaker 2: So they are quantum in a meaningful way, but there's
Speaker 2: still relatively small in terms of quid account. And crucially,
Speaker 2: they're noisy. They make errors a lot noisy.
Speaker 1: That doesn't sound good for computation.
Speaker 2: It's the main challenge. It limits the complexity and length
Speaker 2: of taculs we can run reliably, and the hardware itself
Speaker 2: it's quite diverse. You've got superconducting circuits cooled down to
Speaker 2: near absolute zero. There are trapped ions using lasers to
Speaker 2: hold and manipulate individual atoms. Also neutral atoms, photonics, lots
Speaker 2: of different approaches being researched, huge engineering efforts.
Speaker 1: Wow. Okay, so the hardware is complex, developing and noisy.
Speaker 1: That sounds challenging to program for Do I need a
Speaker 1: PhD in quantum physics just to write Hello World on
Speaker 1: one of these?
Speaker 2: Hah? Thankfully No, that's another common worry, But it's not
Speaker 2: necessarily the case. You can actually learn quantum computing through
Speaker 2: quantum programming. Focus on the problem solving aspects first.
Speaker 1: How does that work? Isn't the physics kind of.
Speaker 2: Essential it underlies everything? Yes, But there's a quantum software stack,
Speaker 2: much like the classical one we're used to. It provides
Speaker 2: layers of abstraction hardware control the bottom than middle layers
Speaker 2: for optimization, error handling, and then the tools. Programmers use
Speaker 2: quantum languages like q shag or quiscuit, their compilers, libraries,
Speaker 2: and really importantly, quantum simulators.
Speaker 1: Simulators.
Speaker 2: Yeah, like emulators exactly. They run on your classical computer
Speaker 2: and mimic a quantum computer. We'll come back to why
Speaker 2: they're so vital. But the point is you can start
Speaker 2: programming without mastering all the deep quantum mechanics. First, what
Speaker 2: you do need is some comfort with basic linear algebra.
Speaker 1: Linear algebra, okay, vectors, matrices, complex numbers, that's.
Speaker 2: The language, yes, because quantum states are represented by vectors
Speaker 2: and operations are matrices. If you have that foundation, you
Speaker 2: can start building algorithms that.
Speaker 1: Actually sounds manageable, more like learning a new programming paradigm
Speaker 1: than becoming a physicist overnight. It is.
Speaker 2: Think of it that way.
Speaker 1: Okay, so if we're programming, let's imagine we're sitting down
Speaker 1: to write some quantum code. What are the absolute basic
Speaker 1: things we can do? The fundamental operations. Yeah, like the
Speaker 1: quantum equivalent of setting a variable or doing an addition.
Speaker 2: Right. You can break down almost any quantum algorithm into
Speaker 2: three fundamental stages. First, state preparation. You start with your quivots,
Speaker 2: usually all in a default state, typically the zero state,
Speaker 2: analogous to a classical bit being off.
Speaker 1: Okay, starting point all zeros.
Speaker 2: Then you need to transform them into the state you
Speaker 2: actually want for your computation. Often this involves creating a
Speaker 2: superposition that's.
Speaker 1: The zero in one at the same time thing sort of.
Speaker 2: Yeah, it's a combination, a blend of zero and one
Speaker 2: are each with a certain weight or amplitude for a
Speaker 2: single quibit. A key tool is the right theta gate.
Speaker 2: Think of it like a knob you turn to set
Speaker 2: the precise mix of zero and one R.
Speaker 1: Got it. Prepare the initial mix. What's next?
Speaker 2: Second unitary transformations We usually just call them gates. These
Speaker 2: are the quantum logic operations. They act on the quibbits
Speaker 2: to change their states, maybe entangle them with other cribits.
Speaker 2: So like A and D or are not gates in
Speaker 2: classical computing analogous, yes, but with a crucial difference. All
Speaker 2: quantum gates must be reversible.
Speaker 1: Reversible meaning you can always undo them exactly.
Speaker 2: If you know the output state and the gay used,
Speaker 2: you can perfectly determine the input state. This isn't true
Speaker 2: for many classical gates, like A and D. You can't
Speaker 2: tell from the output zero whether the inputs were zero, one, zero,
Speaker 2: one zero or zero zero.
Speaker 1: Huh. Why is reversibility so important?
Speaker 2: It's fundamental to quantum mechanics. Information can't just be destroyed.
Speaker 2: It means we have to design algorithms differently, carefully preserving
Speaker 2: information instead of discarding intermediate results.
Speaker 1: Okay, prepare the state, apply reversible gates. What's the third step,
Speaker 1: third measurement. This is the only way to get information
Speaker 1: out of the quantum computer.
Speaker 2: To read the result makes sense.
Speaker 1: You have to measure to see what happened.
Speaker 2: But here's the quantum catch. If your quibit is in
Speaker 2: a superposition, the measurement outcome is probabilistic, non deterministic.
Speaker 1: WHOA, So you don't get a definite answer.
Speaker 2: You get a definite answer either zero or one for
Speaker 2: a single quibit, but which answer you get is determined
Speaker 2: by probability. The probability of getting say zero I, is
Speaker 2: related to the square of its amplitude in the superposition
Speaker 2: before measurement.
Speaker 1: So it's like the computation explores many possibilities via superposition,
Speaker 1: but when you look you only see one randomly chosen
Speaker 1: based on those possibilities.
Speaker 2: That's a decent picture. Yeah, The measurement collapses the superposition
Speaker 2: into a single classical outcome.
Speaker 1: That sounds incredibly tricky to debug. If you run the
Speaker 1: same code twice, you might get different answers just due
Speaker 1: to the measurement randomness.
Speaker 2: You absolutely can, especially if the final state is a
Speaker 2: complex superposition. And this brings us back to the simulators
Speaker 2: I mentioned.
Speaker 1: Ah The classical programs that mimic quantum computers.
Speaker 2: Yes, they are a programmer's best friend, especially when learning
Speaker 2: why because on a simulator you can cheat.
Speaker 1: Cheat how you can.
Speaker 2: Pause the simulated execution and peak at the internal quantum state.
Speaker 2: Look at all the amplitudes the full superposition without simulating
Speaker 2: a measurement.
Speaker 1: I see, you can see the probabilities before the dice
Speaker 1: roll happens.
Speaker 2: Exactly. You can see the full quantum state. That's a
Speaker 2: luxury you absolutely do not have on real quantum hardware.
Speaker 2: A real measurement is irreversible and collapses the state.
Speaker 1: So simulators are crucial for development and debugging, especially for
Speaker 1: smaller problems where simulation.
Speaker 2: Is feasible invaluable. You test, debug, understand your algorithm on
Speaker 2: a simulator first.
Speaker 1: Okay, so we have our building blocks. Prepare the state, supersition,
Speaker 1: transform it reversible gates, and measure probabilistic outcome. Got it?
Speaker 1: How do we use these blocks to actually solve something useful,
Speaker 1: maybe something faster than a classical computer. Let's connect this
Speaker 1: to a real problem. You mentioned search problems earlier. How
Speaker 1: could these quantum operations help with something like well, the
Speaker 1: n Queen's puzzle that's a classic computer science.
Speaker 2: Problem, right. The n Queen's puzzle is a great example,
Speaker 2: and it leads us to one of the most famous
Speaker 2: quantum algorithms, Grover's search.
Speaker 1: Grover Search. I've heard that what's the core idea.
Speaker 2: The core idea is providing a speed up for unstructured search.
Speaker 2: Imagine you have a huge list of items and items
Speaker 2: and a function that can tell you if an item
Speaker 2: is the one you're looking for, but you have no
Speaker 2: other information, no sorting.
Speaker 1: Nothing, so you just had to check them one by one.
Speaker 2: Classically, yes, on average, you'd expect to check about end
Speaker 2: two items in the worst case n items. We call
Speaker 2: that on complexity. Grover's algorithm, using quantum superposition and a
Speaker 2: clever trick called amplitude. Amplification can find the item in
Speaker 2: roughly o er in calls to that checking function square
Speaker 2: root events.
Speaker 1: So for a million items instead of a million checks,
Speaker 1: is more.
Speaker 2: Like a thousand exactly. It's a quadratic speed up. Now
Speaker 2: quadratic isn't the exponential speed up that quantum computing is
Speaker 2: truly famous for, like Shores algorithm for factoring, But for
Speaker 2: certain problems we're iren is still a massive improvement over in.
Speaker 1: Okay, so Grover's speeds up search. How do we apply
Speaker 1: it to end Queen's We need that checking function first, right,
Speaker 1: a function that tells us if a certain arrangement of
Speaker 1: queens is a valid.
Speaker 2: Solution precisely, and we need to implement that classical TI
Speaker 2: checking function on a quantum computer. This brings us back
Speaker 2: to reversible.
Speaker 1: Computing, because all quantum gates are reversible.
Speaker 2: Right. We can't just compute FX where x is the
Speaker 2: board arrangement and FX is true or false if it's
Speaker 2: a solution, and then discard X. We have to preserve
Speaker 2: the input.
Speaker 1: How do we do that?
Speaker 2: Typically we use an extra enciloquibit. We transform a state
Speaker 2: x zero, input x and selo zero into exocuibit. If
Speaker 2: sx is true one, the incilla flips to one. If
Speaker 2: false zero, it stays zero. The input x ra is
Speaker 2: preserved ice.
Speaker 1: So the answer is stored in the extra cubit without
Speaker 1: losing the original board state. What kind of gates do that?
Speaker 2: Multiquibit Gates like cnot control not T, and especially to
Speaker 2: fully gates C cnot controlled, controlled not T are the
Speaker 2: building blocks for creating these reversible classical functions.
Speaker 1: Okay, so we can build a reversible n queen's checker. Now,
Speaker 1: how do we represent the board x itself?
Speaker 2: Ah, And this is where it gets really interesting. This
Speaker 2: is the AHA moment for quantum algorithm design. How you
Speaker 2: encode the problem matters enormously for a fency. Let's take
Speaker 2: end four, a small four x four board. A naive
Speaker 2: encoding might be use one quibit for each square on
Speaker 2: the board one if there's a queen, zero if not.
Speaker 1: Okay, that's four x four sixteen quibits.
Speaker 2: Right, and the total number of possible states is two
Speaker 2: hundred and sixteen, which is sixty five five hundred and
Speaker 2: thirty six. Grover's algorithm would have to search through this
Speaker 2: huge space. That's a lot of quibits and potentially many
Speaker 2: many Grover iterations needed.
Speaker 1: Seems inefficient? Is there a better way?
Speaker 2: Definitely and optimize encoding leverages the rules of the puzzle.
Speaker 2: We know there must be exactly one queen per row, right,
Speaker 2: so instead of representing every square, we just need to
Speaker 2: represent the column position of the queen in each row.
Speaker 2: For n four, the columns are zero, one, two, three.
Speaker 2: How many bits do you need to represent those numbers?
Speaker 1: Two bits? Can represent four values zero, zero, zero, one.
Speaker 2: Ten, eleven exactly, so two bits per row and there
Speaker 2: are four rows. That means we only need four rows
Speaker 2: two bits row. What is eight quippits total?
Speaker 1: Wow, eight quibits instead of sixteen. That's a big difference.
Speaker 2: And look at the search space. It's now just the
Speaker 2: number of ways to place one queen in each row,
Speaker 2: which is four choices for the first row, four for
Speaker 2: the second, and so on. Forty four equals two hundred
Speaker 2: and fifty six states, two.
Speaker 1: Hundred and fifty six instead of sixty five thousand, five
Speaker 1: hundred and thirty six.
Speaker 2: That's massively smaller, huge difference. Now, searching two hundred and
Speaker 2: fifty six states with Grover takes far fewer iterations, roughly
Speaker 2: two hundred and fifty six, which is about sixteen, but
Speaker 2: the exact number is around nine iterations for n four.
Speaker 2: Compare that to searching sixty five thousand, five hundred and
Speaker 2: thirty six states. It's potentially hundreds or thousands of iterations.
Speaker 1: That really drives the point home. The cleverness isn't just
Speaker 1: in the Grover algorithm itself, but in how you set
Speaker 1: up the problem for the quantum computer.
Speaker 2: The encoding is key, It's absolutely fundamental. Choosing the right
Speaker 2: representation can be the difference between a feasible quantum solution
Speaker 2: and one that's completely impractical.
Speaker 1: This n Queen's example is powerful. It shows the potential
Speaker 1: but also the subtlety. But it also brings us back
Speaker 1: to that in ice Q thing you mentioned earlier, the noise.
Speaker 1: How do these algorithms actually perform on real current noisy hardware,
Speaker 1: and what's the path forward to running larger problems reliably?
Speaker 2: That's the billion dollar question. Really. The NISQ devices we
Speaker 2: have today, well, they struggle with noise. Error rates are
Speaker 2: relatively high. Running grovers for even moderately large n or
Speaker 2: more complex algorithms is extremely challenging. The errors accumulate and
Speaker 2: destroy the computation.
Speaker 1: So what's the solution? Just build better, less noisy hardware.
Speaker 2: That's part of it, definitely, but the long term vision,
Speaker 2: the ultimate goal is fault tolerant quantum computers.
Speaker 1: Fault tolerant meaning they can correct their own errors.
Speaker 2: Exactly using principles of quantum error correction. It's conceptually similar
Speaker 2: to error correction in classical computers or communication, but much
Speaker 2: more complex due to the nature of quantum states.
Speaker 1: How does it work?
Speaker 2: Roughly, the core idea is redundancy. You don't use just
Speaker 2: one physical quibut to represent the information you care about,
Speaker 2: instead a single logical quibit. The quibit your algorithm actually
Speaker 2: uses is encoded across many physical quibots on the hardware.
Speaker 1: So one quibot in my program might actually be say
Speaker 1: ten or one hundred physical quibots working together.
Speaker 2: It could be hundreds or even thousands, depending on the
Speaker 2: code and the desired level of protection. This introduces enormous.
Speaker 1: Overhead overhead, meaning extra resources.
Speaker 2: Massive extra resources. A single logical gait operation in your
Speaker 2: algorithm might require performing dozens, hundreds, maybe more operations on
Speaker 2: the underlying physical quibuts to detect and correct errors while
Speaker 2: preserving the quantum state, and some operations are particularly costly,
Speaker 2: things like arbitrary rotations those rye or RS gates we
Speaker 2: mentioned for state preparation. Implementing them tolerantly often requires something
Speaker 2: called magic states.
Speaker 1: Magic states sounds exotic, they are.
Speaker 2: There are special, highly entangled resource states that have to
Speaker 2: be prepared and consumed, adding significant complexity and cost to
Speaker 2: the computation. It's like needing a very rare, expensive tatalyst
Speaker 2: for certain reactions.
Speaker 1: Okay, so fault tolerance is the goal, but it comes
Speaker 1: at a potentially huge cost in terms of the number
Speaker 1: of physical quibits and operations needed. This sounds like it
Speaker 1: pushes practical quantum computing further into the future.
Speaker 2: It's definitely a long term engineering challenge. We're talking potentially
Speaker 2: millions of physical quibits for some applications. But it also
Speaker 2: highlights why just looking at the theoretical number of steps
Speaker 2: in an algorithm like oh you friend for Grover isn't
Speaker 2: the full story.
Speaker 1: You need to consider the physical cost of running it
Speaker 1: reliably precisely.
Speaker 2: And that's where another set of crucial tools comes in
Speaker 2: Quantum resource estimator.
Speaker 1: What do they do? Estimate the resources needed?
Speaker 2: Yes, tools like the Azure Quantum Resource Estimator allow you
Speaker 2: to take your quantum algorithm, specify assumptions about the future
Speaker 2: fault tolerant hardware like quibet quality operation speeds, and it
Speaker 2: estimates what you'd actually need.
Speaker 1: What kind of estimates does it give?
Speaker 2: It estimates the total number of physical creepits required and
Speaker 2: the expected runtime to execute your algorithm fault tolerantly. This
Speaker 2: gives a much more realistic picture of the true cost.
Speaker 1: Can we apply that back to our end For Queen's example,
Speaker 1: the optimized one with eight logical quivits, what would that
Speaker 1: look like on a fault tolerant machine.
Speaker 2: It's a great test case running the resource estimator on
Speaker 2: that optimized and for grover Search, well, depending on the
Speaker 2: specific assumptions about the error correction code and hardware, you
Speaker 2: might be looking at something like, say three minutes of runtime,
Speaker 2: but requiring around three hundred and forty thousand physical quipits.
Speaker 1: Three hundred and forty thousand physical equipments for just eight
Speaker 1: logical quibuts solving a tiny four by four puzzle.
Speaker 2: That's the kind of overhead we're talking about for fault tolerance. Now,
Speaker 2: these numbers depend heavily on the assumptions, but it illustrates
Speaker 2: the scale, and this kind of analysis is vital. It
Speaker 2: lets us compare the projected cost of a quantum solution
Speaker 2: against the best known classical solutions for a problem, not
Speaker 2: just comparing Grover's N against a naive n classical search.
Speaker 2: We need realistic comparisons to see where the true quantum
Speaker 2: advantage might lie for practical problems.
Speaker 1: That's incredibly sobering but also clarifying. It paints a much
Speaker 1: clearer pickre sure of the challenges and the path ahead.
Speaker 2: Wow, we've really covered a lot of ground today, from
Speaker 2: cutting through that initial hype around quantum computing replacing everything,
Speaker 2: to understanding its real niche in specialized problems, peeking inside
Speaker 2: the noisy hardware and getting a feel for the basic
Speaker 2: programming steps, preparing states, applying gates, measuring and seeing Grover's
Speaker 2: algorithm in action with En Queen's highlighting just how critical
Speaker 2: that probleming coding is. Then finally facing the reality of noise,
Speaker 2: fault tolerance and the massive resources needed. It's been quite
Speaker 2: the journey, it really has. I think the key takeaways
Speaker 2: are quantum computing is specialized, not general purpose. It holds
Speaker 2: immense promise for fields like material science and drug discovery,
Speaker 2: but we're still in the early noisy and ic Q days.
Speaker 2: Fault tolerance through error correction is the goal, but it
Speaker 2: brings significant overhead and as we saw, clever algorithm design
Speaker 2: and smart problem in coding are crucial. Plus we need
Speaker 2: tools like resource estimators to realistically gauge the path to
Speaker 2: practical advantage.
Speaker 1: Absolutely that in Queen's encoding example really stuck with me.
Speaker 1: How drastically changing the representation altered the feasibility. It makes
Speaker 1: you think, what other classical problems out there, Maybe problems
Speaker 1: we think are just hard, could potentially unlock a quantum
Speaker 1: speed up if we just found a more ingenious way
Speaker 1: to encode them for a quantum computer. Something for all
Speaker 1: of you to maybe ponder, what problem could you reimagine
Speaker 1: for the quantum realm. That's all the time we have
Speaker 1: for this deep dive. Thanks for joining us and keep
Speaker 1: exploring