ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: Why I Left Quantum Computing Research

If someone was to make a fully functioning quantum computer tomorrow, then at least at the moment we wouldn't have all that much we could do with it.

I left quantum computing in 2020 after finishing an applied maths PhD in that field from Cambridge University.

And the thing is, I loved being a researcher, and I even got offered a few postdocs kind of informally when I was thinking about leaving.

But I still decided to leave.

And the reason is because I became a little bit concerned

that maybe quantum computers aren't going to be as useful in the real world as we might have hoped.

Well, it's been five years and a lot has happened in quantum computing.

So I want to go back and see

if my assessment from back then is really still looking like it's going to be true.

So yeah, on the hardware front, I think things are actually going really well.

In 2020, when I left, most quantum computing companies only had like dozens of qubits.

I think the state of the art might have been like around 50.

But now there are companies that have like 10 times that.

And what's even more impressive is

that most companies seem to be on track with what they said they would be able to do in 2025.

I mean, I'm not an expert in that area, but it seems like despite all the incredible challenges engineering -wise to make it happen,

these companies are doing it, which is awesome.

I have no idea when fully functioning quantum computers will be built, but some people say 10 years or 20 years, and I have no reason not to believe that.

I think that we really are on track.

But on the software front, I think things have been much more disappointing.

To explain why, I need to explain one of the most misunderstood things about

quantum computers quantum computers are not just general purpose supercomputers it's not like every single thing

that you do on a regular computer can be done faster

on a quantum computer like you can't play games faster on

a quantum computer in fact quantum computers are very specialized at doing very specific tasks

and I want to explain a little bit about quantum computers

so you can see why that is I'm not going to explain what a qubit is

or anything like that because

if you want to see a full explanation of like exactly

how a quantum computer works then I have a video

that you can watch where I built a small kind of useless quantum computer at home

and hopefully that explains it a little bit better.

But right now what I want to do is give you

an overview of like how you should be thinking about what a quantum computer can do and what it can't do.

Let's say that this is a normal computer that does this algorithm.

Every time you put in a bit string if I put that into my computer, I type it in, then it will output some other bit string.

There's some sort of computation that this program is doing to the bit string to make it something else.

Well, what happens when you replace this computation with the quantum version of this computation?

So let's say this is my amazing quantum computer.

It acts in a very similar way where

if you put in this bit string you'll get out another bit string

but the thing that you can do with quantum computers

that you can't with a classical computer is you can put in more than one input at a time.

In fact you can put in every possible bit string

and the output instead of being a single number will be

a combination of all of the outputs for every single one of the inputs.

This seems miraculous right?

Like you can do an exponential amount of computation with the same amount of resources

that a regular computer would take to just do one computation.

This seems like there needs to be some sort of catch, and there is.

You see, it is true that a quantum computer truly does do this whole amount of computation all at the same time,

but the problem is when you try and ask the quantum computer what the answer is.

So, with a regular computer, you might expect it just outputs this whole thing in a way that's readable.

But with a quantum computer, you have to then go and measure this output.

And when you measure it, what happens is it collapses to just one of these things randomly.

So all the rest is completely gone and you just have one number that comes out and it's just a random computation.

And that is not useful.

You see, even though technically a quantum computer really does do a huge

amount of parallel computation at once with the same cost that a regular computer would take to just do one computation,

it's not possible to easily extract all that information.

In fact, you can extract very little from the output, and if you do it in the naive way of just trying to look at the answer, you just get a random answer.

So, how are quantum computers ever going to be useful then?

Well it turns out for very particular types of computations

that it is possible to extract just slightly more information from this whole huge list

that lets you actually get out something very useful.

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It often feels like we can't change the world, but for the individual animals that your donations will help it makes all of the difference.

So if you want to donate, the link is in the description and I would really appreciate it.

Now back to the video.

So let me give you the classic example, Shaw's factoring algorithm.

So the problem that Shaw's algorithm is trying to solve is called factoring.

So say I give you this massive number, then the factoring problem asks what are the two numbers that you have to multiply to get that massive number?

In other words, can you factorize this number?

And it turns out this is really hard to do on a classical computer.

Like yes you can do it by sort of just checking every single factor but that will take you ages and ages.

But in 1999 Peter Shaw showed

that actually you can solve this problem quite quickly on a quantum computer the first step of

that algorithm does look like putting in a huge superposition of all the inputs into a quantum computer

and calculating the output but the next step is not to just go

and measure this because as we already saw doing

that just collapses the state and is totally useless

so instead shaw's algorithm uses a very clever way of manipulating this state further

so you put this back into another quantum computer doing a whole lot more steps

and you end up with some sort of quantum state here where now it is possible to measure this state

and actually get out a little bit of useful information and

that is just enough to find out how to factorize that huge number.

But the point is you have to come up with some very clever manipulation at this part of the quantum computer.

A particular algorithm that is going to take out just the useful bit of information

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so that at the end you can read out what that information is.

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But designing this algorithm is hard and it depends exactly what the problem you want to solve is.

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And some problems like factoring have turned out to be amenable.

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So it turns out there are a bunch of problems like

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that where we have been able to come up with some very clever quantum algorithm

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that does let us extract the information we want and solve the problem.

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But for most hard problems that you might want to solve with a quantum computer, we haven't yet found quantum algorithms.

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That might be because no one has yet found it,

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or it could be that there is no algorithm to find that quantum computers just can't solve that class of problem.

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And we never know which it is until someone eventually finds a quantum algorithm for that problem.

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So you can see why quantum algorithm design is quite tricky.

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You really never know which problems you're going to be able to solve with a quantum computer or not.

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When Shor's algorithm came out in 1994 there was a lot of enthusiasm

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that we would be able to find many more problems like factoring

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that quantum computers would be able to do

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so much better than classical computers

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and yet by the time I was leaving my PhD in 2020 I was pretty disappointed by the lack of progress.

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There were of course other quantum algorithms like for example phase estimation

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which is actually very related to Shaw's factoring algorithm

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and also another one called Hamiltonian simulation which is quite different and allows you to simulate quantum objects.

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But now five years later I want to go back and see if well there's any new algorithms,

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but also whether there have been new applications found for the existing algorithms.

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And the spoiler is, I think it's been a little bit disappointing, but there have been some hopeful signs.

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Let's start with quantum machine learning.

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So we've all seen how incredibly good AI systems are, and I think there's this worry that sometimes gets brought up in the media

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that when we have full quantum computers we're going to have these supercharged AIs.

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fact when I was in the field quantum machine learning was super popular

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and there seemed to be papers being churned out about it all the time

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but I was very very skeptical that any of those would amount to much

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because I just don't think that quantum computers are very well suited to machine learning problems.

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Quantum computers often exploit structure and you know machine machine learning algorithms are all about putting in tons of data.

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And the data isn't particularly structured, it's like whatever examples, for example, from the internet.

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So I just don't think this is playing to quantum computers' strength.

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And it seems like more and more people have gone off this quantum machine learning idea.

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So I don't think that's going to be a big deal.

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But there is another area that gets heaps of hype that may be more justified.

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In fact, when I was in quantum computing,

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this was the area of quantum computing I was most excited for and which I was ultimately most disappointed by.

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It's quantum chemistry.

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Simulating molecules is a big deal, for example in medicine where you want to see how a particular drug is going to interact with,

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for example, proteins in your body.

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And so there was a lot of promise about quantum computers being able to speed up these very important chemical simulations.

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At the time, that seemed justified to me because quantum computers are able to do something called ground state energy estimation.

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And they seem to do it exponentially more quickly than a classical computer.

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But it turned out there's actually an assumption in there that kind of makes the whole thing invalid.

145

So let me explain.

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Say you had these two chemicals and you want to know what's the chance

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that they're going to bond together and how hard is it going to be to make them do that.

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One way to get a handle on that is to estimate the energy of both of these states.

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So you can look at this state and you can look at its energy

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and you can look at this whole state and look at its energy

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and if this energy is significantly lower than that one then it's more likely to end up in this state.

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Estimating these ground state energies is super important

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and there's a quantum algorithm called phase estimation that seems to do it super well.

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This is the gist of it, you put in a blank qubit that you're going to write the answer onto

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and you also put in this qubit which is in the ground state.

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Now if you have this ground state

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and the blank qubit you can just put them straight into your quantum computer

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that does phase estimation

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and then the blank qubit ends up coming out of this

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computation with the ground state energy just written straight onto it

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and you can read that out and find out what the energy was.

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So this seems perfect because this algorithm runs really fast and

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so it seems like you can find out the ground state energy of any molecules which would be huge.

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But unfortunately the trick is this thing.

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So this is a state, a qubit state, which represents the ground state of the molecules.

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In other words you need to already know what the ground

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state of those molecules is to be able to run this algorithm.

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You can kind of see the problem right?

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But at the time I thought that this would be fine

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because there are ways to estimate what the ground state would look like more

171

or less and as long as there's a good overlap between your guess

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and the reality then you end up with something that is good enough to find the energy.

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But there's no guarantee that your guess and the actual ground state will have all that much overlap.

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It was actually a couple of friends of mine who weren't in quantum computing

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but did know a whole lot about chemistry and biology

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that actually pointed this out to me they made me see

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that there's actually no guarantee that this overlap is big and

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so it's not clear at all

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that quantum computers can solve this ground state energy problem

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and in fact in 2022 there was a paper called is there evidence for exponential quantum advantage

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and it was written by a whole lot of very respected scientists in the quantum computing field and also in chemistry,

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and so their conclusion was this.

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We conclude that evidence for such an exponential advantage across chemical space has yet to be found.

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While quantum computers may still prove useful for quantum chemistry,

185

it may be prudent to assume exponential speedups are not generically available for this problem.

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So yeah, quantum computers aren't actually a silver bullet for solving quantum chemistry problems.

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But there is a class of problems that's quite related that quantum computers are surprisingly good at, and that's quantum simulations.

188

Quantum simulations are actually what inspired quantum computing.

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The idea is simple.

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Quantum systems are actually very hard to simulate in general.

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If you try to for example simulate what a bunch of electrons are doing when they interact with each other quantum mechanically, you find that on a regular computer this is really really hard to do.

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The number of possible states inside of the superposition become exponential

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and so it's hard even on a classical computer to write down all of these states.

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And so quantum simulation has always been a difficult problem.

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But for a quantum computer it's actually quite easy.

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So what quantum computers can do is they can simulate the way

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that electrons evolve by having their qubits sort of interact with each other in a similar way.

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And by doing that analog they can give us a pretty good picture of what the original electrons would have done.

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And this turns out to be very useful.

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So here are some examples of what we might do with quantum simulations.

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So you might have heard of these things called superconductors.

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Superconductors are these kind of magical materials that have no electrical resistance.

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That means that when you use them in a circuit they don't lose energy.

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But the problem with superconductors is that the ones

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that we know of have to be cooled down quite a lot to actually work.

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So one thing people dream of is coming up with high temperature superconductors.

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So we just have to figure out the exact materials to use to make this superconductor

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so that it has zero resistance even at room temperature

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and quantum computers might help with that

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because it's really hard to simulate these kind of materials before you actually make them

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because of those problems I was talking about earlier it's hard to figure out what the electrons are all doing

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and so the easiest way to do it at the moment is just to make all of these materials

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and then test them in the lab but that's really difficult

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when there's so many different materials that you can make

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and test so the idea is

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that you know once we eventually have a quantum computer we

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would be able to basically just computer simulate what all of these different materials are going to do

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and then just pick out the most promising candidates to use in the lab

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and I think that that's a very exciting application.

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There are similar problems like this like for example solar cells you might know

221

that at the moment our solar cells are like somewhere around I think 20 -25 % efficient which is pretty good,

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like they can take 25 % of the energy that falls on them and convert that into electricity.

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But that is actually near the limit of what's possible with silicon.

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The hope is that we'll be able to find different types of solar cells

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that have better efficiencies and people have already started working on these

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and have examples of them

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but hopefully once we have a quantum computers we'll be able to simulate way more materials

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and find something with really high efficiency because computing the efficiency

229

that a particular material is going to have before you actually make it in the lab

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and test it is again very hard there's many other examples like

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that like for example nitrogen fixation we want to figure out how we can do

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that much more efficiently than the current harbour Bosch process.

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And there's also applications inside of science so for example you might have heard

234

that quantum computers can be used to simulate some models of black holes and

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that may be somewhat useful for you know people who are

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studying this to understand what kind of behaviour those sort of black holes would have.

237

So yeah I think that there are actually a lot of applications in this area that are very promising.

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So that is great but this isn't a new algorithm system.

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Hamiltonian simulation is a fairly old algorithm.

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Where are the new algorithms?

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Well, amazingly and very unexpectedly, in the last five years there has been at least one new algorithm.

242

So in 2023 a group of researchers proved

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that a particular problem with a random oracle could be solved

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on a quantum computer exponentially faster than could be solved on a classical one

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that is really cool as a theoretical result

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but this random oracle bit means

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that it can't actually be made in practice still i am actually really hopeful about

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that because usually

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when there are algorithms of this sort where they're sort of

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relative to some oracle often people come up with a way to take that idea and put it into a practical algorithm.

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Like I think Shor's algorithm even came out of sort of oracle -like results like that.

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So that's proof that there really are other quantum algorithms out there and I think it's really exciting.

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If you work in quantum computing you might have worried

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that like most of my videos sounded quite negative and I'm really sorry about that.

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I actually don't want to come down too hard on quantum computing.

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I just think

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that there has been a bit of a misallocation where a

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lot of work has of course gone into the hardware

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because it's a very hard problem and a lot of work has gone into for example quantum error correction.

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But it just seems like quantum algorithms have been neglected.

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If a quantum computer appeared tomorrow, we need things that we're going to use that quantum computer for.

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So we need to be searching for good quantum algorithms.

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But at least when I was in the field, it wasn't very popular to work on quantum algorithms.

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And that's because it had a reputation for being really hard.

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Which makes sense, because so few algorithms have been found.

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And even when new algorithms are found, often there's a mistake with them or people show

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that actually classical computers can do that too and

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so it's quite a hard field to be working in but I think it's one of the most important.

269

So yeah, I'm hopeful about quantum computers but I think that there is a lot of work left to be done.

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คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้

วิดีโอนี้มี 269 ประโยค และ 3682 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 21:08 ผู้พูดพูดด้วยความเร็วเป็นธรรมชาติ ประมาณ 174 คำต่อนาที ใกล้เคียงกับบทสนทนาในชีวิตประจำวัน 83% ของคำอยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ ส่วนที่เหลือควรดูไว้ก่อนเริ่มฝึก

คำศัพท์สำคัญในวิดีโอนี้

คำที่พบไม่บ่อย 15 คำจากวิดีโอ พร้อมคำอ่านและความหมาย:

quantum /ˈkwɑntəm/ (คำนาม) — จำนวน. The total amount of something; quantity.
algorithm /ˈælɡəɹɪðm̩/ (คำนาม) — ขั้นตอนวิธี, อัลกอริทึม. A collection of ordered steps that solve a mathematical problem. A precise step-by-step plan for a computational procedure that possibly begins with an input…
solve /sɒlv/ (คำกริยา) — แก้, แก้ไข. To find an answer or solution to a problem or question; to work out.
output /ˈaʊtpʊt/ (คำนาม) — เอาท์พุต. Production; quantity produced, created, or completed.
string /stɹɪŋ/ (คำนาม) — ด้าย, เชือก. A long, thin and flexible structure made from threads twisted together.
chemistry /ˈkɛm.ɪ.stɹi/ (คำนาม) — เคมี. The branch of natural science that deals with the composition and constitution of substances and the changes that they undergo as a consequence of alterations…
molecule /ˈmɑ.lɪ.kjul/ (คำนาม) — โมเลกุล, อณู. The smallest particle of a specific element or compound that retains the chemical properties of that element or compound; two or more atoms held together by…
efficiency /ɪˈfɪʃ.ən.si/ (คำนาม) — ประสิทธิภาพ. The extent to which a resource is used for the intended purpose.
cage /keɪd͡ʒ/ (คำนาม) — กรง. An enclosure made of spaced bars, normally to hold animals while providing some ventilation.
donate /ˈdoʊˌneɪt/ (คำกริยา) — บริจาค. To make a donation; to give away something of value to support or contribute towards a cause or for the benefit of another.
guarantee /ˌɡæɹ.ənˈtiː/ (คำนาม) — การรับประกัน. Anything that assures a certain outcome.
disappointed /ˌdɪsəˈpɔɪnɪd/ (คำคุณศัพท์) — ผิดหวัง, สิ้นหวัง. Defeated of expectations or hope; experiencing disappointment; let down.
hardware /ˈhɑɹdˌwɛɹ/ (คำนาม) — ฮาร์ดแวร์. Fixtures, equipment, fasteners, tools, and devices used for general-purpose construction and repair of a structure or object. Also such equipment as sold as…
collapse /kəˈlæps/ (คำกริยา) — พัง, ถล่ม. To break apart and fall down suddenly; to cave in.
researcher /ˈɹiˌsɝ.t͡ʃɚ/ (คำนาม) — นักวิจัย. One who researches.

กริยาวลีที่คุณจะได้ยิน

put in (คำกริยา) — ใส่. To place inside.
go back (คำกริยา) — กลับไป. To return to a place or state after having been there at a previous time.

การออกเสียงที่ควรระวัง

ผู้พูดใช้รูปย่อและรูปลดเสียง 33 ครั้ง เช่น can't, don't, I'm ให้พูดแบบสั้นตามที่ได้ยิน

เสียง “th”: algorithm /ˈælɡəɹɪðm̩/, mathematics /mæθ(.ə)ˈmæt.ɪks/, enthusiasm /ɪnˈθuziæzəm/, theoretical /ˌθi.əˈɹɛt.ɪ.kəl/
เสียง “sh” และ “zh”: simulation /ˌsɪm.jəˈleɪ.ʃn̩/, efficiency /ɪˈfɪʃ.ən.si/, reputation /ˌɹɛpjʊˈteɪʃən/, explanation /ˌɛkspləˈneɪʃən/, efficient /ɪˈfɪʃənt/
คำยาว — ลงเสียงหนักให้ถูกพยางค์: efficiency /ɪˈfɪʃ.ən.si/, disappointed /ˌdɪsəˈpɔɪnɪd/, reputation /ˌɹɛpjʊˈteɪʃən/, explanation /ˌɛkspləˈneɪʃən/, electricity /ɪˌlɛkˈtɹɪsɪti/

วิธีฝึกกับวิดีโอนี้

ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
เริ่มที่ความเร็ว 0.75× พูดตามทีละประโยค แล้วกลับไปใช้ความเร็วปกติเมื่อเริ่มคล่อง
อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง quantum, algorithm, solve เป็นพิเศษ

ไวยากรณ์ในวิดีโอนี้

โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:

โครงสร้าง	ในวิดีโอ
Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน	has happened · have been · have actually made
Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ	will be built · can be done · is called
Relative clauses who / which + อนุประโยค — ข้อมูลเพิ่มเติมเกี่ยวกับคนหรือสิ่งของ	donated, which is · simulation which is · qubit which is

เทคนิค Shadowing คืออะไร?

Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ

เทคนิค shadowing: อ่านคู่มือฉบับเต็มทีละขั้นตอน →

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