Speaker 1: You know that feeling right when you need to get
Speaker 1: up to speed on something really complex fast, but you're
Speaker 1: just drowning in articles and papers.
Speaker 2: Yeah, totally information overload exactly.
Speaker 1: So we're here to give you that shortcut, that deep
Speaker 1: dive to get you properly informed. Today we're jumping into
Speaker 1: a field that's honestly reshaping how we even think about computation.
Speaker 2: It really is. We're talking quantum computing, and our mission
Speaker 2: today is to untack the core ideas, look at the
Speaker 2: tools making it accessible, which is amazing, and even touch
Speaker 2: on some algorithms that sound like science.
Speaker 1: Fiction, they really do. Our main guide here is the
Speaker 1: second edition of Learn Quantum Computing with Python and IBM
Speaker 1: Quantum by Robert Laredo. He's an IBM Mastered inventor, a
Speaker 1: quantum ambassador, just brilliant and.
Speaker 2: A great guy too. Donated his previous books royalties to
Speaker 2: Doctors Without Borders. We're also pulling in insights from reviewers
Speaker 2: like Haci Norlen, Mikhail Grossey, Sean Wagner, all deep in
Speaker 2: the IBM Quantum and caskit world.
Speaker 1: So whether you're prepping for that big meeting or just
Speaker 1: trying to figure out what all the buzz is about.
Speaker 1: Or maybe you're just super curious, which you should be. Huh. Yeah,
Speaker 1: this deep dive should give you those aha moments. We're
Speaker 1: aiming to show how these really abstract ideas are becoming concrete.
Speaker 2: Tools, right without making your head explode hopefully.
Speaker 1: Okay, so let's start at the beginning. Quantum mechanics sounds intimidating,
Speaker 1: but it's the bedrock of all this. We're talking about
Speaker 1: the rules governing the universe at the tiniest scales, the
Speaker 1: sub atomic level.
Speaker 2: Yeah, the foundations laid by you know, the giants Schrodinger, Plank, Heisenberg,
Speaker 2: Drach Einstein. Their work described a world that's just fundamentally
Speaker 2: weird compared to our everyday experience, and that weirdness is key, right,
Speaker 2: Like the double slit experiment exactly, that's a classic. You
Speaker 2: fire single particles like photons at two slits, you'd expect
Speaker 2: two lines on the screen behind it, right, Yeah, it
Speaker 2: makes sense, but you don't get that. You get an
Speaker 2: interference pattern like waves canceling out and adding up. It
Speaker 2: shows particles behaving like waves. This wave part duality. It's
Speaker 2: not just a cool fact, it's something Quantum computers actively use.
Speaker 1: So we all get classical bits on off zero one
Speaker 1: transistors doing their thing. But the quantum version, the quibit. Yeah,
Speaker 1: that's the heart of it. What makes it so different.
Speaker 2: Well, the huge difference is superposition Equibit isn't just zero
Speaker 2: or one. It can be zero, it can be one,
Speaker 2: or it can be a combination of both at the
Speaker 2: exact same.
Speaker 1: Time, both like simultaneously.
Speaker 2: Kind of think of it less like a hidden state
Speaker 2: like a spinning coin before it lands, and more like, well,
Speaker 2: the quantum state itself is a blend of possibilities until
Speaker 2: you measure it. Measuring forces it to choose zero or one.
Speaker 1: You mentioned a spinning coin, but you said it's not
Speaker 1: like that. Yeah, that the quantum outcome is truly random.
Speaker 1: That sounds profound. Are we saying there's no hidden instruction
Speaker 1: determining the outcome?
Speaker 2: That's exactly what standard quantum mechanics says. Yeah, it's fundamentally probabilistic,
Speaker 2: not just we don't know enough, but actually random. And
Speaker 2: that's a massive conceptual leap. It means quantum computers work
Speaker 2: with probabilities directly.
Speaker 1: Wow, Okay, that changes things.
Speaker 2: It does, and that leads to the second key, difference entanglement.
Speaker 2: This is the one Einstein called famously spooky action at
Speaker 2: a distance.
Speaker 1: Spooky action. I love that phrase. What does it actually
Speaker 1: mean for quibits?
Speaker 2: It means you can link two or more quibits together
Speaker 2: in a special way, so intimately linked that measuring the
Speaker 2: state of one quibbit instantly tells you the state of
Speaker 2: the other, no matter how far apart they are, instantly
Speaker 2: across any distance, instantly. If you have two entangled quibits
Speaker 2: and measure one is zero, you know the other is zero.
Speaker 2: If one's one, the other's one, always correlate. It's like
Speaker 2: they're still connected, sharing the same fate. It's a crucial resource.
Speaker 1: Okay, superposition entanglement. Yeah, and you mentioned interference. Going back
Speaker 1: to the double slit thing. How does that play into algorithms?
Speaker 2: Right? Interference Quantum algorithms are designed to leverage this. Think
Speaker 2: of it like paths leading to the wrong answer interfere
Speaker 2: destructively and cancel each other out.
Speaker 1: Ah, so they just disappear effectively.
Speaker 2: Yeah, while the paths leading to the right answer interfere constructively,
Speaker 2: reinforcing each other. So the probability of measuring the correct
Speaker 2: answer gets amplified. It's a way to sift through possibilities incredibly.
Speaker 1: Efficiently and visualizing this yeah, it's hard to picture. Are
Speaker 1: there tools for that?
Speaker 2: There are. For a single quibut, we often use the
Speaker 2: block sphere. It helps visualize its state, including its amplitude
Speaker 2: and phase, like a point on a globe. But for
Speaker 2: multiple entangled quibits that gets complicated. So there's a more
Speaker 2: powerful tool called the q sphere, which can show the
Speaker 2: combined state of many quibits, including those crucial phase relationships
Speaker 2: between them.
Speaker 1: This is fascinating, but maybe a bit abstract. How do
Speaker 1: people actually use this stuff? Where do you start?
Speaker 2: Great question? For many. The entry point is the IBM
Speaker 2: Quantum Platform or IQP, and it's got a cool history.
Speaker 2: IBM actually put the first quantum computer on the cloud
Speaker 2: free for anyone to use, back in May twenty sixteen.
Speaker 1: Wow, twenty sixteen. That feels like ages ago in this field, right.
Speaker 2: It was called the IBM Quantum Experience.
Speaker 1: Then.
Speaker 2: Now it's this much more developed platform, the ITP.
Speaker 1: What's actually on the platform.
Speaker 2: It's got a few key parts. The main platform view
Speaker 2: is your dashboard. You can see which quantum computers are online,
Speaker 2: how busy they are, check on your jobs, see how
Speaker 2: much computing time you've.
Speaker 1: Used, like a mission control kind of. Yeah.
Speaker 2: Then there's the documentation which is huge. Everything you need
Speaker 2: to set up your coding environment, understand Kuskit, which we'll
Speaker 2: get to its APIs. It's all there.
Speaker 1: And you mentioned learning resources.
Speaker 2: Oh yeah, the learning section is fantastic courses, tutorials from
Speaker 2: beginner stuff right up to advanced topics, even quantum safe cryptography.
Speaker 2: It really lowers the barrier to entry inside the platform.
Speaker 1: I heard there's something called the composer, like a drag
Speaker 1: and drop interface.
Speaker 2: That's right. The composer is brilliant for visual learners or
Speaker 2: just starting out. It's a graphical interface where you literally
Speaker 2: drag quantum gates onto quibit lines to build a circuit.
Speaker 1: Oh cool.
Speaker 2: And the best part is the real time feedback. As
Speaker 2: you build, you see visualizations update instantly, the state vector
Speaker 2: view showing amplitudes, the probabilities view predicting outcomes. Even that
Speaker 2: q sphere we talked about, you can pop a not
Speaker 2: t git on there and see the probability flip from
Speaker 2: one hundred percent zero to one hundred percent one right away.
Speaker 1: So you can really see cause and effects.
Speaker 2: Exactly. It makes it much more intuitive.
Speaker 1: Okay, but what if you want to go beyond dragging
Speaker 1: and dropping. You mentioned quiske It.
Speaker 2: Yes, kiss git. That's IBM's open source software development kit
Speaker 2: for quantum computing. It's built on Python, which makes it
Speaker 2: accessible to a huge community of developers. It's really the
Speaker 2: standard for working with IBM's quantum systems programmatically.
Speaker 1: Is it just one monolithic.
Speaker 2: Thing or no, It's cleverly layered. They think about different
Speaker 2: developer roles. At the lowest level. You have kernel developers.
Speaker 2: They're working right down near the hardware, figuring out the
Speaker 2: precise microwave pulses needed to manipulate the quibbits.
Speaker 1: Really deep stuff, okay, Hardware whisperers, ah yeah.
Speaker 2: Then you have algorithm developers. They're using quiskit to design
Speaker 2: the quantum circuits themselves. The algorithms. A big focus for
Speaker 2: them is minimizing the impact of noise, making things run
Speaker 2: better on today's imperfect hardware.
Speaker 1: Right noise. We'll definitely need to talk more about that
Speaker 1: and the third layer model developers.
Speaker 2: These are folks who might be say chemists or financial analysts.
Speaker 2: They want to use a quantum algorithm as part of
Speaker 2: a larger workflow. They use quisk it to integrate quantum
Speaker 2: computation into their existing applications, focusing on getting data in
Speaker 2: and results out, often without needing to know every single
Speaker 2: gate detail.
Speaker 1: That makes a lot of sense. Can you run quiske
Speaker 1: it on your own machine?
Speaker 2: You can. You set it up locally, usually with Anaconda
Speaker 2: for Python and Jupiter notebooks. It's great for simulating circuits,
Speaker 2: but crucially you need your IBM Quantum API token to
Speaker 2: actually send jobs to the real quantum computers in the cloud.
Speaker 1: Gotcha, And if you get stuck.
Speaker 2: There is a huge, very active community the quiskeuters on Slack.
Speaker 2: It's amazing. You can ask questions, get help, see what
Speaker 2: others are working on, connect with researchers. It's a really
Speaker 2: supportive environment.
Speaker 1: That's great to know. So we have quippits, we have
Speaker 1: platforms like IBM Quantum and tools like quisket. Now, how
Speaker 1: do we actually control the quibbuts? You mentioned gates in
Speaker 1: the composer.
Speaker 2: Right, Quantum logic gates. They are the fundamental operations you
Speaker 2: perform on quibbots. But unlike classical gates, which just flip
Speaker 2: bits based on simple rules, quantum gates are well, they're different.
Speaker 2: They perform unitary transformations.
Speaker 1: Unitary transformations sounds fancy. What does that mean? Practically?
Speaker 2: It essentially means there are rotations in the Quibbets abstract
Speaker 2: mathematical space. Think of the block sphere. A gate rotates
Speaker 2: the Quibbitts state vector to a different point on that sphere.
Speaker 2: And crucially, these transformations preserve the vector's length, which relates
Speaker 2: to probability.
Speaker 1: Okay, rotations, and you mentioned they're.
Speaker 2: Reversible, almost all of them. Yes, reversibility is a key property.
Speaker 2: If you apply a gate, you can almost always apply
Speaker 2: another related gate its complex conjugator inverse to get back
Speaker 2: exactly where you started. A HaTamar gate, for instance, applied twice,
Speaker 2: brings you back to the initial state, unlike some classical
Speaker 2: gates exactly think of a classical A and D gate.
Speaker 2: If the output is zero, you don't know if the
Speaker 2: inputs were zero, zero, zero, one, or ten. Information is lost.
Speaker 2: Quantum gates generally don't lose information like that.
Speaker 1: Okay, So what are some of the essential gates the
Speaker 1: building blocks.
Speaker 2: Well, for single clivits, the HaTamar gate or H gate
Speaker 2: is absolutely fundamental. It's the main way we create superposition.
Speaker 2: It takes a definite zero A state or one eight
Speaker 2: to state and puts it into an equal mix of both.
Speaker 1: The superposition workhorse. Got it.
Speaker 2: Then you have the polygates X, Y, and Z. These
Speaker 2: are basic rotations by one hundred and eighty degrees around
Speaker 2: the respective axes on the block sphere. The X gate
Speaker 2: is the quantum equivalent of the classical not T gate.
Speaker 2: It flips zero to one a's and vice versa.
Speaker 1: Simple flips what else.
Speaker 2: For more precise control, especially over the quibt phase, which
Speaker 2: is cre interference, you have phase gates, the ES gate,
Speaker 2: the T gate, and their inversus s dagger T tigger.
Speaker 2: They rotate the state around the z axis by specific.
Speaker 1: Amounts, fine tuning the phase exactly.
Speaker 2: And then there's the universal U gate. It's the most
Speaker 2: general single quibut gate. With just three angle parameters, it
Speaker 2: can perform any possible rotation, any single quibbit operation you
Speaker 2: can imagine.
Speaker 1: Wow, Okay, so that covers single quibts. But the real
Speaker 1: power comes from interactions, right.
Speaker 2: Entanglement precisely, yeah, and that requires multiquibit gates. The absolute
Speaker 2: cornerstone here is the controlled knot gate or.
Speaker 1: Cnot controlled not. What does it do?
Speaker 2: It acts on two quibbits, a control quibbit and a
Speaker 2: target quibt. If the control clibit is in the zero state,
Speaker 2: it does nothing to the target. But if the control
Speaker 2: quibit is in the one A state, it applies an
Speaker 2: X gate a not gate to the target quibt flipping it.
Speaker 1: Ah. So one quibt state controls an operation on another.
Speaker 2: Yes, and this is the primary way we create entanglement
Speaker 2: between equipbitts. It's fundamental.
Speaker 1: What are the multiquibit gates are important?
Speaker 2: Well, there's the Tafoli gate or CCX. It's like a
Speaker 2: cnot but with two control quibots. The target quibit only
Speaker 2: flips if both control quibots are one.
Speaker 1: A okay, double control.
Speaker 2: It's significant because the Toafoli gate is actually a universal
Speaker 2: classical gate. You can build any classical circuit using just
Speaker 2: to fullness in quantum. It shows how complex operations are
Speaker 2: built up, often requiring decomposition into many simpler gates like hadamards, cnots.
Speaker 1: And T gates interesting any others.
Speaker 2: The swapgate is sometimes useful. It just swaps the states
Speaker 2: of two quibuts. Does exactly what it says on the tin.
Speaker 1: Okay, So you said almost all gates are reversible. What's
Speaker 1: the exception where does the information get lost?
Speaker 2: The exception is measurement. When you measure a quiboit to
Speaker 2: find out if it's a zero or a one, the
Speaker 2: quantum state collapses. All that complex superposition information, the amplitudes,
Speaker 2: the phase. It's gone. You just get a classical bit
Speaker 2: zero or one based on the probabilities dictated by the
Speaker 2: state before measurement. It's the one irreversible step that makes sense.
Speaker 1: You have to look eventually, and looking.
Speaker 2: Changes things exactly. And you know, a really beautiful simple
Speaker 2: circuit that uses these basic gates is the one that
Speaker 2: creates the Bell state.
Speaker 1: Bell states.
Speaker 2: They're the four fundamental states of maximum entanglement. For two quibits.
Speaker 2: For example, the A plus A state is an equal
Speaker 2: superposition of zero, zero and eleven air. Both quibits are
Speaker 2: zero or both are one perfectly correlated.
Speaker 1: And how do you make that.
Speaker 2: It's surprisingly simple. You start with two quibits and the
Speaker 2: zero zero state, apply a Hadamar gate to the first
Speaker 2: quibut to put it in superposition. Then apply a Cnot
Speaker 2: gate with the first quibut as control and the second
Speaker 2: is target boom bell state maximally entangled.
Speaker 1: Wow, just two gates for that spooky action.
Speaker 2: Yep, it's foundational for so many quantum algorithms and protocols.
Speaker 1: Okay, so we've got the theory, the gates, the tools.
Speaker 1: But running this on actual hardware, I imagine it's not
Speaker 1: quite as clean as the simulations. You mentioned Noise earlier.
Speaker 2: Oh yeah, noise is the big, big, big challenge in
Speaker 2: the current era of quantum computing. These quantum states are
Speaker 2: incredibly fragile. But first, let's talk about how you run
Speaker 2: things efficiently on real hardware. That's where quisket run time
Speaker 2: comes in.
Speaker 1: Run time, what's that about.
Speaker 2: It's IBM service designed to execute quantum circuits more effectively
Speaker 2: on their cloud systems. It's about getting better performance and
Speaker 2: managing the whole process better than just sending individual isolated jobs.
Speaker 1: How does it do that?
Speaker 2: One key feature is sessions. Imagine you have an algorithm
Speaker 2: that needs to run a quantum circuit, then do some
Speaker 2: classical processing, then run another related circuit, maybe iteratively.
Speaker 1: Right, like machine learning or optimization.
Speaker 2: Exactly if you submitted each circuit as a separate job
Speaker 2: you keep going back to the end of the queue.
Speaker 2: For the quantum computer, it would take forever and the
Speaker 2: system's state might change between your runs. Sessions guarantee you
Speaker 2: dedicated access to a quantum processor for a block of
Speaker 2: related computations. It's huge for reducing weight times and ensuring consistency.
Speaker 1: That sounds essential for complex tasks really is.
Speaker 2: And within run time you interact using primitives. These are
Speaker 2: like standardized interfaces for common quantum tasks. They abstract away
Speaker 2: some of the hardware variation, like what kind of tasks.
Speaker 2: The main ones currently are the sampler and the estimator.
Speaker 2: The sampler primitive, for example, is designed for algorithms where
Speaker 2: you primarily care about the probability distribution of the measurement outcomes.
Speaker 2: You run the search up many times and it gives
Speaker 2: you back the counts or quasi probabilities for each result.
Speaker 1: Okay, so run time and primitives help manage the execution,
Speaker 1: But what about the noise itself? What are we actually
Speaker 1: fighting against?
Speaker 2: Right the noise? There are a few main culprits. The
Speaker 2: big one is decoherence. This is the general loss of
Speaker 2: that delicate quantumness.
Speaker 1: Losing the quantum magic.
Speaker 2: Pretty much It breaks down into two main types. There's
Speaker 2: T one time, or relaxation time. That's a long it
Speaker 2: takes for a quibit in an excited state like one,
Speaker 2: you to naturally decay back down to its ground state zero,
Speaker 2: like a hot cup of coffee.
Speaker 1: Cooling down, okay, losing energy.
Speaker 2: Then there's T two time, or dephasing time. This is
Speaker 2: about losing the phase coherence, the precise alignment of the
Speaker 2: quantum wave. It's caused by random fluctuations in the Quibbitts environment.
Speaker 2: Think of it like trying to keep a group of
Speaker 2: spinners perfectly synchronized. When there's random jittering, they drift apart.
Speaker 2: T two is usually much shorter than T one, making
Speaker 2: phase control really tricky.
Speaker 1: So energy loss and phase drift what else?
Speaker 2: You also have gate errors. The physical pulses used to
Speaker 2: implement quantum gits aren't perfectly precise, so rotation might be
Speaker 2: slightly off or introduce unwanted phase shifts and finally, readout errors.
Speaker 2: Even measuring the quibet's final state isn't perfect. Sometimes you
Speaker 2: measure a zero when it was actually a one, or
Speaker 2: vice versa.
Speaker 1: Well, it sounds like a mindfield. How do you even
Speaker 1: know how bad the noise is on a particular machine.
Speaker 2: Quisket actually lets you characterize the noise. You can run
Speaker 2: calibration experiments to measure T one T two gate errors,
Speaker 2: readout errors for a specific device, and then build a
Speaker 2: noise model based on that data. You can use this
Speaker 2: model in simulations to predict how noise will affect your
Speaker 2: algorithm before running it on the real thing.
Speaker 1: That's useful. So knowing the enemy, how do you fight it?
Speaker 2: Two main strategies, error suppression and aer mitigation suppression. First,
Speaker 2: air suppression techniques try to actively reduce the effect of
Speaker 2: noise during the computation itself. A key example is dynamical decoupling.
Speaker 2: During times when equip it is supposed to be idle
Speaker 2: waiting for other operations, it's vulnerable to dephasing. Dynamical decoupling
Speaker 2: inserts sequences of carefully timed pulses, often simple excates that
Speaker 2: effectively refocus the corbet's phase, canceling out some of the
Speaker 2: slow drift caused by noise.
Speaker 1: Like constantly nudging it back on track.
Speaker 2: Kind of yeah. It actively fights the diffusion of the
Speaker 2: quantum state.
Speaker 1: Okay, and error mitigation, how is that different?
Speaker 2: Aerror. Mitigation doesn't necessarily prevent the errors from happening. Instead,
Speaker 2: it assumes errors will happen, runs the computation anyway, often
Speaker 2: multiple times with variations, and then uses classical post processing
Speaker 2: techniques to estimate what the ideal noise free result would
Speaker 2: have been.
Speaker 1: AH, so you clean up the results afterwards using.
Speaker 2: Statistics essentially yes techniques like zero noise extrapolation ZNE, where
Speaker 2: you deliberately amplify the noise and extrapolate back to the
Speaker 2: zero noise point, or probabilistic error cancelation PECK, which learns
Speaker 2: the errors and tries to invert them statistically. Quis GET
Speaker 2: run Time incorporates these into different resilience levels you can choose.
Speaker 2: They use extra classical computation, but are crucial for getting
Speaker 2: useful answers from today's noisy intermediate scale quantum and ice
Speaker 2: Q devices and ice Q that's.
Speaker 1: The current era, right noisy, not quite fault tolerant yet exactly.
Speaker 2: We don't have enough quibits or low enough error rates
Speaker 2: for full quantum error correction yet, so suppression and mitigation
Speaker 2: are absolutely vital.
Speaker 1: This battle against noise really highlights the engineering challenge, but
Speaker 1: it's leading somewhere exciting the idea of quantum advantage right.
Speaker 2: Quantum advantage is the point where a quantum computer can
Speaker 2: solve a specific problem faster or more accurately than the
Speaker 2: best possible classical computer, regardless of how big or powerful
Speaker 2: that classical computer is.
Speaker 1: And are we seeing signs of that we are?
Speaker 2: IBM Quantum had a significant result in twenty twenty three
Speaker 2: using their one hundred and twenty seven quo bit Eagle processor.
Speaker 2: They demonstrated calculations related to material science that were complex
Speaker 2: enough to be beyond exact simulation by even the most
Speaker 2: powerful classical supercomputers using brute force methods. It was a
Speaker 2: major step showing potential for quantum utility even before fault tolerance.
Speaker 1: That's huge. So what kinds of algorithms actually deliver this
Speaker 1: speed up? Can we look at some example?
Speaker 2: Absolutely, some of the earliest foundational algorithms really showed the
Speaker 2: potential for speed up, even if the problems they solved
Speaker 2: were a bit academic.
Speaker 1: Like Deutsch's algorithm okay, wud that do?
Speaker 2: It solves a very simple problem. You have a function
Speaker 2: that takes one bit in and gives one bit out.
Speaker 2: Is the function constant always output zero or always outputs
Speaker 2: one or balanced output zero for one input one for
Speaker 2: the other.
Speaker 1: Classically, you'd have to test both inputs.
Speaker 2: Right, two checks exactly two queries to the function. Quantumly,
Speaker 2: Deutsch's algorithm solves it with just one query. It uses
Speaker 2: superposition and interference to check both possibilities at once.
Speaker 1: One query instead of two. Okay, maybe not world changing,
Speaker 1: but it proved the concept.
Speaker 2: It was revolutionary at the time. Then came Deutsch Jozza.
Speaker 2: It generalizes the idea to functions with ended inputs. Determining
Speaker 2: if that kind of function is constant or balanced Classically
Speaker 2: could take potentially many queries up to two to the
Speaker 2: power of n one plus one in the worst.
Speaker 1: Case, exponentially harder.
Speaker 2: Right, quantumly still just one query.
Speaker 1: One query. That's astonishing.
Speaker 2: It really demonstrates the power of quantum parallelism. And then
Speaker 2: there's Bernstein Vaserani. It finds a hidden secret string used
Speaker 2: in a specific type of function. Classically, you need end
Speaker 2: queries where n is the length of the string. Quantumly,
Speaker 2: again just one query.
Speaker 1: Okay. These sound amazing, but maybe like mathematical puzzles, did
Speaker 1: they connect to real world problems?
Speaker 2: They were crucial stepping stones. They proved speed ups were
Speaker 2: possible and inspired further insearch. They laid the groundwork for
Speaker 2: algorithms with more or direct applications, like the quantum Fourier
Speaker 2: transform QFT.
Speaker 1: QFT related to the classical Fouria transform.
Speaker 2: Very much, so it's a quantum version that operates on
Speaker 2: quibit amplitudes. It's incredibly important because it's a key component
Speaker 2: in some of the most powerful quantum algorithms we know,
Speaker 2: most famously Shores algorithm for factoring large numbers.
Speaker 1: AH, the one that potentially breaks modern cryptography.
Speaker 2: That's the one. QFT allows Shores algorithm to find the
Speaker 2: period of a function exponentially faster than any known classical method,
Speaker 2: and finding that period is the key to factoring.
Speaker 1: So QFT is a vital subroutine. What about searching? Is
Speaker 1: there a quantum way to find a needle in a
Speaker 1: haystack faster?
Speaker 2: There is? That's Grover's search algorithm. Imagine you have a
Speaker 2: huge unsorted database or list with n items, and you're
Speaker 2: looking for one specific item.
Speaker 1: Classically, you might have to check on average half of
Speaker 1: them right and two worst case all in correct.
Speaker 2: Grover's algorithm gives you a quadratic speed up. It can
Speaker 2: find the item in roughly the square root of n iterations,
Speaker 2: so for a million items, instead of five hundred thousand checks,
Speaker 2: it's more like one thousand.
Speaker 1: Square root en that's still a significant speed up for
Speaker 1: large end. How does it work.
Speaker 2: It's clever. It uses two main steps repeatedly. First, an
Speaker 2: oracle operation marks the target item, usually by flipping its phase,
Speaker 2: making it negative. It doesn't know which item is right,
Speaker 2: but it can recognize it if.
Speaker 1: It sees it, so it tags the winner right.
Speaker 2: Then. The second step is a diffusion operator, sometimes called
Speaker 2: amplitude amplification. This operation essentially takes the average amplitude of
Speaker 2: all items and reflects every item's amplitude about that average.
Speaker 2: The result is that the tagged items amplitude, which was negative,
Speaker 2: gets boosted significantly, while all the others get reduced using
Speaker 2: interference again exactly. You repeat these two steps about sen
Speaker 2: in times, and the probability of measuring the correct item
Speaker 2: becomes very high.
Speaker 1: Okay, these algorithms are powerful, but building them gate by
Speaker 1: gait dealing with noise models, it sounds complex for someone
Speaker 1: who just wants to apply quantum to their field.
Speaker 2: It definitely can be, and that's why IBM introduced quisket patterns.
Speaker 2: It's a higher level way of thinking about and building
Speaker 2: quantum applications patterns.
Speaker 1: Like design patterns and software.
Speaker 2: Engineering sort of.
Speaker 1: Yeah.
Speaker 2: The idea is to provide reusable templates or workflows that
Speaker 2: abstract away a lot of the low level complexity. It
Speaker 2: lets computational scientists or domain experts focus more on their
Speaker 2: problem and less on the nitty gritty quantum circuit details
Speaker 2: or hardware specifics.
Speaker 1: How does it work? Is their standard process?
Speaker 2: Yes. Quisket patterns generally follows four steps. First is map.
Speaker 2: This is where you define your problem and figure out
Speaker 2: the best way to encode your classical data or problem
Speaker 2: structure into a quantum circuit or.
Speaker 1: State, getting the problem into quantum language precisely.
Speaker 2: Second is optimize. This step takes the con sexual circuit
Speaker 2: and adapts it for the real world. It involves transpiling
Speaker 2: it for the specific quantum hardware you'll use, and crucially
Speaker 2: applying those error suppression and mitigation techniques.
Speaker 1: We talk making it robust.
Speaker 2: Third is execute. This is where you actually run the
Speaker 2: optimized circuit, usually using qris gut runtime, and it's primitives
Speaker 2: like the sampler on your chosen back end could be
Speaker 2: a simulator or a real quantum device.
Speaker 1: Run the job, and.
Speaker 2: Fourth is post process. You take the raw results from
Speaker 2: the quantum computer, which might be probability distributions or measurement counts,
Speaker 2: and process them, analyze them, maybe feed them into a
Speaker 2: classical algorithm, and format them into the answer you need
Speaker 2: for your application.
Speaker 1: Map, optimize, execute, post process. That sounds much more manageable.
Speaker 1: Can you give an example?
Speaker 2: Sure, The book uses a fun one called the rock
Speaker 2: band problem. Imagine you have a logical expression describing which
Speaker 2: musicians can play together. Maybe the drummer and bassis have issues,
Speaker 2: but the singer needs the guitarist. Things like that. You
Speaker 2: want to find valid band combinations.
Speaker 1: Okay, a constraint satisfaction problem exactly.
Speaker 2: Using quiskit patterns with Grover's algorithm, you wouldn't necessarily need
Speaker 2: to manually build the oracle circuit that recognizes valid bands.
Speaker 2: You could potentially just define the logical expression itself and
Speaker 2: the pattern would handle mapping it to the appropriate Grover instance,
Speaker 2: optimizing it, running it, and giving you back the satisfying
Speaker 2: assignments with valid band lineups.
Speaker 1: Wow, so it abstracts away the oracle construction. That's huge
Speaker 1: for Grover's algorithm.
Speaker 2: It aims too. Yes, it makes these powerful algorithms much
Speaker 2: more accessible for solving practical problems.
Speaker 1: So what a journey We've gone from the weirdness of
Speaker 1: quantum mechanics superposition, entanglement.
Speaker 2: Interference all the way through the practical tools like the
Speaker 2: IBM Quantum platform and crisket, the gates that manipulate quibits, tackle.
Speaker 1: The challenge of noise and how we fight it with
Speaker 1: suppression and.
Speaker 2: Mitigation, and explore these incredible algorithms like Deutsche Jersa Grovers
Speaker 2: and the concepts behind QFT showing real quantum speed ups.
Speaker 2: And finally, how things like quisket patterns are trying to
Speaker 2: make it all easier to use.
Speaker 1: It really feels like we're moving into a new phase,
Speaker 1: doesn't it, This era of quantum utility where even noisy
Speaker 1: machines can start tackling problems classical computers.
Speaker 2: Struggle with absolutely And that brings us to the final
Speaker 2: thought for you, the listener. As these quantum computers become
Speaker 2: more capable, even before they're perfectly fault tolerant, think about this,
Speaker 2: what problem in your field? Something that seems computationally intractable today, Yeah,
Speaker 2: could potentially be unlocked by this technology. How might you
Speaker 2: start thinking about mapping that problem onto a quantum state,
Speaker 2: preparing for this next wave of computation.
Speaker 1: That's a fantastic question to ponder. The journey really has
Speaker 1: just begun, and who knows what possibilities will open up next.
Speaker 1: It's an exciting time to be watching this space.