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Speaker 1: Hey, everyone, Daniel here. You know you hear the word

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Speaker 1: quantum a lot, like a lot a lot, mostly in

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Speaker 1: places where it makes no sense. I see it on

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Speaker 1: advertisements for pest control companies or financial firms or laser

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Speaker 1: tattoo joints. Okay, lasers actually are kind of quantum, so

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Speaker 1: maybe that one works. But the point is that the

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Speaker 1: word is so ubiquitous that it's come to mean very little.

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Speaker 1: But it does have meaning. It evokes something modern and mysterious,

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Speaker 1: because in the last one hundred years, physics has discovered

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Speaker 1: that the world is very mysterious and operates under very

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Speaker 1: different rules than the ones we are used to the

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Speaker 1: world that we experience, the one that Aristotle and Copernicus

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Speaker 1: and Galleo and Newton and even Einstein we're trying to

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Speaker 1: understand is something of an illusion. When we peel back

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Speaker 1: that layer of reality and see what's happening underneath, we

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Speaker 1: discover that the rules of the microscopic universe are very different,

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Speaker 1: almost alien to our intuition. And so of course people wondered,

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Speaker 1: almost immediately, hey, what can I do with this? Can

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Speaker 1: I take advantage of that to make my video games

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Speaker 1: faster or hack into my school computer and change my

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Speaker 1: grades because hey, what else is fundamental physics?

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Speaker 2: Good form? All right?

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Speaker 1: But jokes aside, quantum computing is something you hear a

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Speaker 1: lot about these days. There's a lot of information and

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Speaker 1: plenty of misinformation out there. Is it just another marketing

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Speaker 1: ploy like quantum wasps appers or is it like the

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Speaker 1: first computing revolution which completely transformed our society, our economy,

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Speaker 1: and our daily lives. So today on the podcast, we're

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Speaker 1: excited to be talking to Professor Scott Arenson, one of

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Speaker 1: the world's top experts in quantum computing and a renowned

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Speaker 1: communicator who can help us answer the question should we

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Speaker 1: be excited about quantum computing? Welcome to Daniel and Kelly's

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Speaker 1: Extraordinary Universe.

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Speaker 3: Hello, I'm Kelly Waiter Smith, and I sort of think

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Speaker 3: I understand how quantum computing works, but I'm never really sure.

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Speaker 4: Hi.

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Speaker 1: I'm Daniel. I'm a particle physicist, and I simultaneously understand

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Speaker 1: and don't understand quantum computing.

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Speaker 3: That's very appropriate. I think I think I understand it

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Speaker 3: enough to get that joke, which makes me feel pretty good.

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Speaker 1: Oh, you're pretty clever.

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Speaker 3: So what is your favorite product that has been pitched

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Speaker 3: as being quantum that makes you laugh.

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Speaker 1: I once sat next to somebody on airplane who told

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Speaker 1: me she was a quantum messuse. I was really wondering

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Speaker 1: about how that worked, and I asked her a bunch

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Speaker 1: of questions and I didn't reveal my expertise, but I

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Speaker 1: didn't learn anything about quantum mechanics, and that conversation just

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Speaker 1: something about.

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Speaker 5: People, you know, yeah, yep.

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Speaker 3: It was it like maybe she's giving you a massage,

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Speaker 3: maybe she's not, and you don't know until you pay.

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Speaker 3: That's the thing that reveals if it happened or not.

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Speaker 1: No, it was more like spooky action at a distance,

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Speaker 1: because she massages you without actually touching you. She like

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Speaker 1: waves her hands nearby, and that somehow, through quantum mechanics

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Speaker 1: influences your muscles and joints and whatever. So she was

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Speaker 1: charging a lot of money. Also, people were paying her

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Speaker 1: big bucks for this effect.

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Speaker 3: I would be so grumpy if I paid for a

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Speaker 3: massage like that, like you really got to get into

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Speaker 3: my muscles if I'm paying you. But anyway, okay, well

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Speaker 3: that's amazing. I once heard it was a quantum self

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Speaker 3: help talk, and the whole time I just couldn't, could

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Speaker 3: not understand how quantum, like how thinking about it from

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Speaker 3: a quantum way was helpful at all, And it just

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Speaker 3: left me completely baffled.

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Speaker 1: I think people just latching onto the idea that the

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Speaker 1: world is fundamentally different from the world we thought it was,

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Speaker 1: that it works in a different way, it follows different rules,

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Speaker 1: and that feels a little bit like magic. You can

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Speaker 1: sort of grab onto that and smear your business with

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Speaker 1: a little bit of that sparkly quantum physics stuff, And

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Speaker 1: this feels like maybe you can do something which seemed impossible.

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Speaker 3: When I think quantum is particularly likely to get used

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Speaker 3: in that way because it's confusing. I mean, maybe it's

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Speaker 3: not confusing to the people who study it, but for

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Speaker 3: like lay people, it just sounds so unclear. And I

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Speaker 3: think that you can feel like you understand it and yeah,

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Speaker 3: but definitely feel like using the word quantum makes you

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Speaker 3: sound smart and then you just run with it. And

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Speaker 3: so I'm excited to see what our audience thinks quantum

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Speaker 3: computing is all about, because I until I married Zach,

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Speaker 3: who likes giving me literally three hour lectures on car

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Speaker 3: rides about what quantum computing is. Well, I'm driving and

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Speaker 3: can't escape. I didn't feel like I knew. So let's

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Speaker 3: see what does the you know, what does the general public,

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Speaker 3: in particular our audience think quantum computing is.

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Speaker 1: So I reached out to our listeners and I asked

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Speaker 1: them what's different about it quantum computer. Here's what listeners

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Speaker 1: had to say.

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Speaker 6: Quantum computers do multiple calculations at once, and each time

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Speaker 6: you add a bit it goes up by exponential numbers faster.

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Speaker 7: But especially they do not overheat.

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Speaker 1: I don't know anything about quantum computers.

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Speaker 8: You can actually get an answer faster because you can

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Speaker 8: determine the probabilities rather than brute forcing it.

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Speaker 9: Quantum computer works with superpositions instead of ones and zeros.

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Speaker 9: That gives it advantages for some calculations, but makes it

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Speaker 9: worse for other calculations.

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Speaker 8: But quantum computer is like having five hundred and eighty

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Speaker 8: thousand people all typing one word of the book, all

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Speaker 8: at the same time, but in order.

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Speaker 4: A quantum computer is different because you'll never show with

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Speaker 4: it to turn it on and off again or off

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Speaker 4: and on again.

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Speaker 1: A quantum particle can be in.

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Speaker 2: A superposition on off or on and off at the

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Speaker 2: same time.

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Speaker 3: A quantum computer can use three bits I think spin up,

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Speaker 3: spin down, and I think something in between.

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Speaker 10: A quantum computer runs on quantum mechanics, which makes it

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Speaker 10: capable of calculating the probabilities in a situation where randomness

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Speaker 10: is involved.

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Speaker 8: Does a quantum computer not use binary code zeros and

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Speaker 8: ones so there can be more options, which.

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Speaker 9: Makes it more powerful.

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Speaker 2: Possibly quantum computers use q bits that are super cooled

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Speaker 2: and have many many states rather than the binary transistor

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Speaker 2: logic that only has off and on.

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Speaker 5: Quantum computing is all about probabilities.

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Speaker 4: With a quantum computer, you can't tell if it's on

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Speaker 4: or off until you hope the box it came in.

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Speaker 11: A quantum computer uses quantum fluctuation for its processor and

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Speaker 11: explores every possible answer instantaneously.

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Speaker 4: How this is useful for our computation is still beyond

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Speaker 4: my understanding.

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Speaker 11: Computer uses quantum technology to search all the random variables

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Speaker 11: that possibly happen and come up a handurd solution.

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Speaker 12: I think quantum computers process really small numbers. Do they

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Speaker 12: do computations on extremely tiny numbers? I'm not sure what

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Speaker 12: is different about a quantum computer that.

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Speaker 10: Makes use of superposition of states to give nearly infinite possibilities.

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Speaker 11: The size of the processor or the flip flops. So

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Speaker 11: now we're down on a quantum level versus the smallest

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Speaker 11: transistors we have at this moment.

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Speaker 5: Other them its fast are really about nothing.

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Speaker 7: What's different about a quantum computer. I don't really know

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Speaker 7: much about how quantum computers work, but there's something fundamentally

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Speaker 7: different about how the bits, essentially we're being.

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Speaker 8: On or off.

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Speaker 7: And that's not something I know the details of.

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Speaker 3: I'm not surprised that our audience had detailed and insightful

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Speaker 3: responses to this question.

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Speaker 4: That is awesome, But you know.

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Speaker 3: You and I decided this is a complicated question, and well,

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Speaker 3: I'm sure Daniel could have explained it on his own.

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Speaker 3: We thought, you know, it might be a good idea

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Speaker 3: to bring in Scott Aronson, who's an expert on quantum computing,

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Speaker 3: has been working on it for a really long time,

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Speaker 3: and so we're bringing you a conversation that's a collaboration

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Speaker 3: between Daniel and Scott Aronson. So, Daniel, where do we start?

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Speaker 1: So I think the place to start with understanding quantum

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Speaker 1: computers is first with the word computer, Like what do

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Speaker 1: we mean when we say computer? And this isn't just

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Speaker 1: like philosophical rabbit hole. I think it's important to think

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Speaker 1: about what a can is, what a computer does, not

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Speaker 1: just the kinds of computers that we have, but other

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Speaker 1: kinds of computers, what possible computers there are, because then

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Speaker 1: we can compare the different kinds of computers, we can

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Speaker 1: understand what they have in common and what they don't

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Speaker 1: have in common. Because computers are not just something that

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Speaker 1: sits on your desk or the thing inside your phone

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Speaker 1: that makes it go. A computer basically is something that computes.

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Speaker 1: It's something that gives you an answer to a question,

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Speaker 1: you know, and that can be something very simple, like

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Speaker 1: you can have a baseball and you throw the baseball.

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Speaker 1: That gives you the answer to what happens when you

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Speaker 1: throw a baseball, and that can be kind of a

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Speaker 1: complicated calculation. You know, you have to take into account

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Speaker 1: gravity and air resistance and the spin of the Earth

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Speaker 1: and all this kind of stuff, and the baseball does

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Speaker 1: that for you. It computes the answer to that question.

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Speaker 1: And that's not a very exciting computer because that's basically

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Speaker 1: all it can do other than let you play baseball.

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Speaker 1: That one just answers one question. In general, we're interested

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Speaker 1: in computers that can do lots of different kinds of calculations,

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Speaker 1: and one kind of computer is you. You know, back

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Speaker 1: in the middle of last century, what they called a

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Speaker 1: computer was a person like at NASA who sat at

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Speaker 1: a table doing calculations pencil and paper to figure out

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Speaker 1: how are we going to shoot this rocket and what

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Speaker 1: angle do we need to go at, because that was

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Speaker 1: the best way to do those calculations. And humans pretty

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Speaker 1: good at doing lots of different calculations. So a computer

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Speaker 1: can include the thing on your desk, the thing in

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Speaker 1: your phone, a baseball, even a person, right, and all

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Speaker 1: these computers, some of them are good at different kinds

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Speaker 1: of calculations, Like the ball is excellent at calculating where

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Speaker 1: the ball is going to go, but terrible at everything else.

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Speaker 1: A human is pretty good at some kinds of things

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Speaker 1: and not that great at other kinds of things. Like

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Speaker 1: a human is really good at doing things like spotting

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Speaker 1: a tiger in the grass. Your brain is really efficient

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Speaker 1: at noticing things that look like predators, not so great

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Speaker 1: at other things like adding up all the numbers between

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Speaker 1: one and a trillion. I mean, maybe you can find

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Speaker 1: some mathematical shortcut, but if you have to actually add

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Speaker 1: them up, it would take you a long time. It's

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Speaker 1: not the kind of thing your brain is efficient at now.

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Speaker 1: The kind of computer where, of course are interested in.

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Speaker 1: It's not baseballs or tigers. It's the kind that are programmable,

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Speaker 1: the kind that can basically do any kind of calculation.

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Speaker 1: And this is something that's kind of incredible that we

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Speaker 1: can even do. We have a question we want answer,

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Speaker 1: it's some calculation we want done, and we build a

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Speaker 1: computer which is like a machine, build a physical objects

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Speaker 1: that we can manipulate in a way to do our calculation.

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Speaker 1: That's the amazing thing about programmable computers that when you

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Speaker 1: build it, you don't have to know what kind of

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Speaker 1: calculations you want it to do. You can build it

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Speaker 1: in such a way that it can do almost any

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Speaker 1: kind of calculation. That's sort of amazing. We're taking a

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Speaker 1: real world problem and then we're building a device that

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Speaker 1: can calculate the answer as long as we can represent

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Speaker 1: that real world problem somehow in the language of that computer.

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Speaker 1: So these are deep issues and fascinating questions. To help

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Speaker 1: us understand what is computing and how quantum computing is

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Speaker 1: different from regular normal computing, we talk to Professor Scott Arenson.

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Speaker 1: He he's a professor of theoretical computer science at the

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Speaker 1: University of Texas at Austin. He's worked with open AI

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Speaker 1: on the theoretical foundations of AI safety. He writes a

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Speaker 1: wonderful blog about quantum computing called stettl Optimized, and he's

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Speaker 1: maybe most famously the author of quantum computing since democratis.

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Speaker 1: And he's a lot of fun to talk to. So

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Speaker 1: we're very glad to have Scott on the show. And

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Speaker 1: our first question for Scott, who's a professor of theoretical

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Speaker 1: computer science, is what is theoretical computer science anyway?

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Speaker 4: So it's a field that's usually considered to have started

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Speaker 4: with Alan Touring in the nineteen thirties, who came up

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Speaker 4: with a theoretical model of what a computer could be,

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Speaker 4: which was called the touring machine.

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Speaker 1: So when Scott is talking about a touring machine, he's

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Speaker 1: not talking about any kind of computer anybody's ever actually built.

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Speaker 1: This is like the computer science equivalent of a thought experiment, right,

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Speaker 1: It's a thought computer. It's something Alan Turing came up with.

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Speaker 1: It's the simplest kind of computer he could imagine, and

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Speaker 1: it's a computer that, again you would never build. But

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Speaker 1: it's very simple. Imagine a computer that has a tape

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Speaker 1: on which there are written numbers zeros and ones, for example,

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Speaker 1: and you can read the tape. It can move the

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Speaker 1: tape forwards and backwards. They can also write to the tape,

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Speaker 1: so you can do all the basic stuff that we

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Speaker 1: think computers can do. Right. It can read in information,

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Speaker 1: it can do a calculation and decide what it's going

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Speaker 1: to do next. It can write onto the tape, so

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Speaker 1: it can modify that. It can store information. And Turing

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Speaker 1: did something really cool, which is that he showed that

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Speaker 1: a Turing machine is very simple, basic kind of thought.

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Speaker 1: Computer can do any kind of calculation that any computer

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Speaker 1: could do. So if it was doable on a computer,

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Speaker 1: you could do it on a Turing machine. That doesn't

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Speaker 1: mean a Turing machine is the best way to do

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Speaker 1: it or the right way to do it, but because

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Speaker 1: the Turning machine is so simple, it let you think

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Speaker 1: about the kind of things computers could do.

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Speaker 2: Well.

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Speaker 1: You could say, if you can do it on a

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Speaker 1: Turing computer, then you could do it on any computer.

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Speaker 1: And it's easier to prove theorems and think about stuff

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Speaker 1: on a Touring computer. And again, a Turing machine doesn't

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Speaker 1: lend me you to thinking about the kinds of computer

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Speaker 1: that we've been building. It can explore the kind of

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Speaker 1: things any computer can do.

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Speaker 4: When Touring used the word computer in the nineteen thirties,

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Speaker 4: you know, that was what it meant people, usually women,

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Speaker 4: who were hired to do computation. And he came up

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Speaker 4: with his model of the Touring machine by trying to

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Speaker 4: idealize what such a person would be doing. You know

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Speaker 4: that they'd be reading symbols on a piece of paper,

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Speaker 4: maybe you know, crossing them off, replacing them with new symbols,

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Speaker 4: you know, moving back and forth on the paper. They

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Speaker 4: would always be doing so according to some rule that

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Speaker 4: they knew or had been taught. There is nothing in

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Speaker 4: the concept that is tied to you know, it has

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Speaker 4: to be built out of transistors that are etched onto

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Speaker 4: wafers of silicon.

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Speaker 2: Right.

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Speaker 4: That happens to be the way that we do it

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Speaker 4: now because it worked so spectacularly. Well, okay, but you

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Speaker 4: know a computer could in principle be made out of

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Speaker 4: billiard balls, right, It could be made out of jets

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Speaker 4: of water.

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Speaker 2: Right.

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Speaker 5: You know, these would maybe not be very reliable.

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Speaker 1: Methods, and also doesn't have to be programmable and infinitely powerful. Right,

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Speaker 1: anytime I build an experiment, I'm building a computer.

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Speaker 4: Until very recently, you know, there was use in analog computers, right,

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Speaker 4: and people just building analog systems to simulate some physical process.

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Speaker 4: You know, at some point digital computing became so good

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Speaker 4: that it eliminated the use cases for that.

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Speaker 1: So it's great that the kind of digital computers we've

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Speaker 1: been building can basically solve any problem. I mean, there

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Speaker 1: are cobbyists to that, there are things turn computers can't do.

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Speaker 1: But the crucial thing for understanding quantum computing and computing

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Speaker 1: more generally is that some problems are hard, and some

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Speaker 1: problems are easy. Some problems you can do quickly, and

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Speaker 1: some problems you can do very very slowly. And different

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Speaker 1: kind of computers are going to be good at different

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Speaker 1: kinds of things, so the same with it. Like you

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Speaker 1: as a person, you are a computer. You are fast

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Speaker 1: or slow at particular problems. Our style of computers, what

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Speaker 1: we call classical digital computers. These are good at some

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Speaker 1: kinds of things and slow at other kinds of things.

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Speaker 1: It's a feature of the kind of computers we've long developed, which,

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Speaker 1: of course you know isn't the only way to do it.

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Speaker 1: That's where we're going. But the origins of the digital

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Speaker 1: computer that we've been using go all the way back

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Speaker 1: to the middle of last century with John von Neuman.

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Speaker 4: John von Neuman, who built some of the first digital computers,

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Speaker 4: was you know, directly inspired by tourings insights, in particular

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Speaker 4: the idea that you didn't want to build a different

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Speaker 4: machine for each possible function. You wanted to build one

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Speaker 4: universal machine and then have it simulate any other machine by.

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Speaker 5: Giving it an appropriate program.

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Speaker 4: This is, you know, an idea that today is so

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Speaker 4: obvious that, you know, it's hard to convince my students

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Speaker 4: that it was ever non obvious. So today what we

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Speaker 4: do in theoretical computer science is often about trying to

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Speaker 4: understand what problems can be solved efficiently.

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Speaker 1: All right, And so when Scott talks about computers being efficient,

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Speaker 1: he's thinking about whether they're good at this kind of problem?

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Speaker 1: Can they do it quickly? As a problem gets bigger

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Speaker 1: and bigger, do they become unbearably slow at solving it?

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Speaker 1: Like you can add up the numbers between one and

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Speaker 1: five pretty quickly, you can add up the number between

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Speaker 1: one and one hundred, little less quickly. If it gets

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Speaker 1: to one in a trillion, it's hopeless, right, And so

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Speaker 1: for digital computers there are some problems that can be

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Speaker 1: solved in principle but would take a very very long time,

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Speaker 1: and the kind of computer we've been building. An example

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Speaker 1: of that problem is checking to see if a number

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Speaker 1: is prime. Like you can tell that eleven is prime

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Speaker 1: because you can't think of any two numbers that multiply

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Speaker 1: themselves together to give you eleven because it is prime.

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Speaker 1: If I give you an arbitrary number seven, four hundred

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Speaker 1: and seventeen, how do you know whether it's prime. Well,

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Speaker 1: a computer can do this by, for example, checking all

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Speaker 1: the numbers that go into it. That's just brute forcing it.

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Speaker 1: There are some more clever algorithms we'll hear about later,

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Speaker 1: but essentially it's very slow at checking prime numbers. So

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Speaker 1: computers can do a lot of things, and the way

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Speaker 1: we've usually built computers are good at some things and

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Speaker 1: slow at other things. So we ask Scott, who thinks

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Speaker 1: about this a lot, what kind of things our normal

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Speaker 1: computers are good at and our normal computers are bad at?

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Speaker 2: Sure?

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Speaker 4: So simulating physics, you know, which you mentioned, is a

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Speaker 4: great example of you know, something that computers have been

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Speaker 4: used for since the very beginning, right and in some sense,

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Speaker 4: you know, the entire program of physics since Galileo and

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Speaker 4: Newton has been to you know, understand nature. Well. It

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Speaker 4: off that we can put the initial conditions into a

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Speaker 4: computer and just have a computer tell us what is

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Speaker 4: going to happen.

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Speaker 5: Simulating a differential equation, you know.

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Speaker 4: In order to model fluids or gravitational dynamics, celestial mechanics.

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Speaker 4: These are our famous examples of things that you know,

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Speaker 4: comput uters.

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Speaker 5: Are very good at. There are issues here, you know,

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Speaker 5: that have to do with discritization.

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Speaker 4: Nature is traditionally modeled in physics as using a continuum

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Speaker 4: of numbers, and computers in the touring sense can only

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Speaker 4: deal with discrete quantities with bits with ones and zeros.

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Speaker 4: So we need some way to deal with that, right,

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Speaker 4: Typically we take continuous quantities and we truncate them, we

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Speaker 4: represent them only to a finite precision, and then you know,

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Speaker 4: we have to understand how.

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Speaker 5: Much error that's going to cause and so forth.

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Speaker 4: But you know, in terms of what computers can do efficiently,

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Speaker 4: you know, I think the classic examples that you know,

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Speaker 4: we would teach in computer science are going to be

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Speaker 4: things like, Okay, you know all of the arithmetic operations

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Speaker 4: that we learn in school, right, adding to integers, you know, multiplying,

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Speaker 4: dividing given Also, you know the thing that Google Maps

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Speaker 4: does for us, right, find the shortest route between two

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Speaker 4: given cities, you know, or between two addresses in the

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Speaker 4: distance between each address and the immediately neighboring ones.

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Speaker 5: Right, it's finding the shortest path in a graph.

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Speaker 4: Okay. Now, there are a bunch of things that turn

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Speaker 4: out to have clever efficient algorithms, even though it is

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Speaker 4: sort of totally not obvious a priori that they would.

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Speaker 4: For example, I give you a bunch of students, you know,

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Speaker 4: I tell you who is willing to be roommates with whom? Okay,

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Speaker 4: and now I ask you pair off as many willing

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Speaker 4: roommates as you can. Right. This is called the maximum

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Speaker 4: matching problem. Find you know, maximum number of pairs of

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Speaker 4: people who are willing to room with each other A priori.

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Speaker 4: That seems like that might require an exponential search, right,

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Speaker 4: It might require considering an astronomical number of possibilities.

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Speaker 5: But it was discovered in the nineteen sixties that it doesn't.

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Speaker 4: There is an efficient way to solve that, That is,

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Speaker 4: there's a way to solve it that scales only like

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Speaker 4: the cube of the number of potential roommates something like that,

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Speaker 4: rather than exponentially.

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Speaker 5: Another great example would be linear.

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Speaker 4: Programming, right, one of the most important problems in industrial

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Speaker 4: you know, operations research, things like that, where you're given

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Speaker 4: a bunch of linear constraints on some variables like this

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Speaker 4: one plus this one can be at most ten, this

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Speaker 4: one minus this one has to be at least eight,

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Speaker 4: and so forth, and you're looking for a solution that

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Speaker 4: satisfies all of those linear constraints. Okay, that also has

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Speaker 4: an efficient solution primality. Right, I give you five thousand

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Speaker 4: digit number, and I ask you is it prime or composite?

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Speaker 2: Right?

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Speaker 5: Well, you know you can try some simple things.

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Speaker 4: You know, if it ends in an even number, or

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Speaker 4: a zero or a five, right, then it's composite. But

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Speaker 4: you know you can check if three, if seven, if eleven,

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Speaker 4: go into it, right, But more generally, right, this is

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Speaker 4: a famous problem in math. It's even extremely important in

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Speaker 4: cryptoc Modern cryptography sort of uses gigantic prime numbers as

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Speaker 4: one of its central ingredients.

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Speaker 5: Okay, Now it turns out that there is.

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Speaker 4: An efficient algorithm that tells you whether a huge number

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Speaker 4: is prime or composite.

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Speaker 5: Okay, it was discovered in the nineteen seventies.

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Speaker 4: There are probabilistic methods, and in two thousand and two

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Speaker 4: even a deterministic method was discovered.

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Speaker 5: Okay, you're very very non obvious.

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Speaker 4: Now, Crucially, these methods only tell you if the number

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Speaker 4: is prime or composite. If it's composite, they don't tell

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Speaker 4: you what the prime factors.

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Speaker 1: Are, all right. So those are some great examples of

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Speaker 1: what classical computers are pretty good at. What kind of

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Speaker 1: things are they slow at? Again, a great example is

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Speaker 1: prime factors. And this is really important because it turns

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Speaker 1: out that it's useful to a lot of people that

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Speaker 1: computers are slow at this. Like, if you know that

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Speaker 1: computers can't crack this puzzle quickly, you can use it

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Speaker 1: as a way to protect your information. The whole field

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Speaker 1: of cryptography, of building codes and protecting information relies on

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Speaker 1: some things being easy and some things being hard for

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Speaker 1: computers to do.

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Speaker 4: The belief that finding the prime factors is hard is

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Speaker 4: actually also central to modern cryptography. So modern cryptography, you

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Speaker 4: have to be able to generate huge prime numbers quickly

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Speaker 4: multiply them together quickly, which we know how to do

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Speaker 4: all of that. That's how we generate the cryptographic keys

441
00:23:32,240 --> 00:23:35,080
Speaker 4: called the public keys, that people can use to send

442
00:23:35,200 --> 00:23:38,080
Speaker 4: us encrypted messages. But then the way that it works

443
00:23:38,320 --> 00:23:41,399
Speaker 4: is that in order to decrypt the message, we.

444
00:23:41,560 --> 00:23:43,560
Speaker 5: Think, you need to know the prime factors.

445
00:23:44,000 --> 00:23:46,720
Speaker 4: If anyone had a fast way to find the prime

446
00:23:46,840 --> 00:23:51,200
Speaker 4: factors of a gigantic composite number, most of the cryptography

447
00:23:51,280 --> 00:23:53,000
Speaker 4: that protects the Internet would be broken.

448
00:23:53,200 --> 00:23:54,800
Speaker 5: Okay, so it is crucial that.

449
00:23:54,880 --> 00:23:58,120
Speaker 4: You know, after half a century of effort, at least

450
00:23:58,160 --> 00:24:02,040
Speaker 4: the best publicly known methods for factoring numbers.

451
00:24:02,160 --> 00:24:02,320
Speaker 8: You know.

452
00:24:02,840 --> 00:24:05,440
Speaker 4: Of course, if the NSA you know knew something better,

453
00:24:05,600 --> 00:24:07,960
Speaker 4: then you know, I would have no reason to know that.

454
00:24:08,320 --> 00:24:11,520
Speaker 4: But the best publicly known methods you know use an

455
00:24:11,520 --> 00:24:15,560
Speaker 4: amount of time that scales exponentially with the number of digits,

456
00:24:15,720 --> 00:24:19,040
Speaker 4: or more precisely, exponentially with the cube root of the

457
00:24:19,160 --> 00:24:20,040
Speaker 4: number of digits.

458
00:24:20,920 --> 00:24:24,400
Speaker 1: So when Scott talks about scaling with the number of digits,

459
00:24:24,600 --> 00:24:26,920
Speaker 1: what he's talking about is how long it will take

460
00:24:27,000 --> 00:24:30,520
Speaker 1: the computer to solve the puzzle, depending on the length

461
00:24:30,960 --> 00:24:34,560
Speaker 1: of the password. Problems that get much harder very quickly

462
00:24:34,640 --> 00:24:37,280
Speaker 1: when you add digits to your password. Those are good

463
00:24:37,400 --> 00:24:40,040
Speaker 1: for cryptography because it makes it easy to make the

464
00:24:40,119 --> 00:24:44,440
Speaker 1: problem impossible even if computers get faster. Like if computers

465
00:24:44,600 --> 00:24:47,520
Speaker 1: suddenly tomorrow get twice as faster next year they're five

466
00:24:47,600 --> 00:24:49,880
Speaker 1: times as fast. You don't want people to be able

467
00:24:49,920 --> 00:24:52,280
Speaker 1: to crack your passwords. The good thing about the prime

468
00:24:52,359 --> 00:24:54,560
Speaker 1: number puzzle is you can just add a couple more

469
00:24:54,640 --> 00:24:57,119
Speaker 1: digits to our keys to our passwords, and now the

470
00:24:57,200 --> 00:25:00,840
Speaker 1: puzzle is impossible. Again, this problem is easy to make

471
00:25:01,160 --> 00:25:05,439
Speaker 1: much harder because it gets exponentially harder as it gets bigger.

472
00:25:05,600 --> 00:25:08,639
Speaker 1: So it's easy to keep making the problem harder faster

473
00:25:08,800 --> 00:25:11,920
Speaker 1: than computers are getting better at the problem. That's the

474
00:25:12,040 --> 00:25:14,680
Speaker 1: key to these exponential problems. And there are a bunch

475
00:25:14,720 --> 00:25:16,960
Speaker 1: of problems like this that are very hard to solve

476
00:25:17,359 --> 00:25:19,560
Speaker 1: for our classical digital computers.

477
00:25:19,960 --> 00:25:23,359
Speaker 4: So factoring is a famous example of a problem that

478
00:25:23,520 --> 00:25:27,480
Speaker 4: might be exponentially hard for all we know. Okay, but

479
00:25:27,800 --> 00:25:31,879
Speaker 4: there's maybe an even more famous class of problems that

480
00:25:31,960 --> 00:25:36,000
Speaker 4: are believed to be exponentially hard. And this includes, like

481
00:25:36,280 --> 00:25:41,359
Speaker 4: most of the problems in combinatorial search and optimization that

482
00:25:41,480 --> 00:25:44,600
Speaker 4: people care about in practice, and so examples would be,

483
00:25:45,200 --> 00:25:49,000
Speaker 4: I give you the distances between every city and every other.

484
00:25:49,440 --> 00:25:52,159
Speaker 4: I ask you to find the shortest path that visits

485
00:25:52,240 --> 00:25:57,800
Speaker 4: every city. That's the famous traveling salesman problem traveling salesperson.

486
00:25:57,480 --> 00:25:58,080
Speaker 5: Call it today.

487
00:25:58,600 --> 00:26:01,680
Speaker 4: Or I give you the mess of a bunch of suitcases.

488
00:26:02,160 --> 00:26:04,200
Speaker 4: I asked, can they all fit in the trunk of

489
00:26:04,240 --> 00:26:06,560
Speaker 4: your car? That's a problem with which many of us

490
00:26:06,640 --> 00:26:07,359
Speaker 4: have experience.

491
00:26:07,720 --> 00:26:10,120
Speaker 1: The answer is always yes, you have to try.

492
00:26:10,080 --> 00:26:12,920
Speaker 4: Arranging them in a different way. Or you know, I

493
00:26:13,000 --> 00:26:15,480
Speaker 4: give you a jigsaw puzzle, you know, can you solve it?

494
00:26:15,640 --> 00:26:17,960
Speaker 4: To make it hard, let's imagine a jigsaw puzzle with

495
00:26:18,080 --> 00:26:21,199
Speaker 4: no picture on it, okay, or a sudoku.

496
00:26:21,600 --> 00:26:23,639
Speaker 1: The answer is always you can solve. It's just a

497
00:26:23,720 --> 00:26:27,199
Speaker 1: question of how many curse words are going to be the.

498
00:26:27,200 --> 00:26:29,919
Speaker 4: Answer yes, yes, And if it's thousands of pieces, then

499
00:26:29,960 --> 00:26:32,679
Speaker 4: are we talking about more curse words than there are

500
00:26:32,800 --> 00:26:34,640
Speaker 4: atoms in the observable universe.

501
00:26:52,200 --> 00:26:54,399
Speaker 1: So we've reminded ourselves what it digital computer can do.

502
00:26:54,680 --> 00:26:57,040
Speaker 1: Some things it can do really well and very quickly,

503
00:26:57,440 --> 00:26:59,520
Speaker 1: and other things it does more slowly, and as a

504
00:26:59,560 --> 00:27:02,760
Speaker 1: problem to bigger, becomes unbearably slow. But the topic of

505
00:27:02,840 --> 00:27:06,080
Speaker 1: today's episode, of course, is other kinds of computers. What

506
00:27:06,240 --> 00:27:09,120
Speaker 1: if you decided to build a computer using something other

507
00:27:09,280 --> 00:27:13,160
Speaker 1: than zeros and ones. Remember, computer is just some arrangement

508
00:27:13,320 --> 00:27:16,000
Speaker 1: of a physical system that lets you do a calculation.

509
00:27:16,560 --> 00:27:19,280
Speaker 1: Doesn't have to be based on like bits that flip

510
00:27:19,359 --> 00:27:22,600
Speaker 1: between zero and one and half precise values. What if

511
00:27:22,640 --> 00:27:25,720
Speaker 1: you found something in the universe that operated differently, that

512
00:27:25,880 --> 00:27:29,360
Speaker 1: wasn't so crisp, right, that operated under a different set

513
00:27:29,400 --> 00:27:32,760
Speaker 1: of rules that you could then exploit to do different

514
00:27:32,840 --> 00:27:35,840
Speaker 1: kinds of calculations, or to have some calculations be faster

515
00:27:36,040 --> 00:27:38,560
Speaker 1: or slower. That would be awesome because it would complement

516
00:27:38,680 --> 00:27:39,879
Speaker 1: our current computer system.

517
00:27:40,000 --> 00:27:40,119
Speaker 2: Right.

518
00:27:40,280 --> 00:27:43,080
Speaker 1: We already know that this is possible in principle. You

519
00:27:43,160 --> 00:27:45,600
Speaker 1: know my example of a baseball that can do a

520
00:27:45,800 --> 00:27:49,160
Speaker 1: very precise calculation that includes all sorts of effect wind

521
00:27:49,240 --> 00:27:51,480
Speaker 1: resistance and the tug of Jupiter and all sorts of

522
00:27:51,520 --> 00:27:54,800
Speaker 1: stuff that would be very laborious for a traditional computer

523
00:27:54,920 --> 00:27:57,159
Speaker 1: to do. It can do it very very quickly, in

524
00:27:57,280 --> 00:27:59,920
Speaker 1: like the time you throw a baseball. But the question,

525
00:28:00,080 --> 00:28:02,800
Speaker 1: and of course, is can you make a programmable computer

526
00:28:03,320 --> 00:28:05,960
Speaker 1: not a specialized one off thing like a baseball, A

527
00:28:06,080 --> 00:28:09,720
Speaker 1: programmable computer that is good at the kinds of problems

528
00:28:10,080 --> 00:28:14,000
Speaker 1: that classical computers are slow at. And that's where quantum

529
00:28:14,000 --> 00:28:18,200
Speaker 1: mechanics comes in, because quantum mechanics does operate under different rules,

530
00:28:18,240 --> 00:28:21,040
Speaker 1: and we can exploit those rules to build a different

531
00:28:21,160 --> 00:28:24,480
Speaker 1: kind of computer. The crucial things to understand about quantum

532
00:28:24,480 --> 00:28:27,240
Speaker 1: mechanics in just a few minutes are that rather than

533
00:28:27,320 --> 00:28:30,880
Speaker 1: having to have definitive states like this bit is zero

534
00:28:31,080 --> 00:28:33,760
Speaker 1: or this bit is one, quantum bits from what we

535
00:28:33,840 --> 00:28:36,720
Speaker 1: call cubits, can be in a superposition of states. A

536
00:28:36,800 --> 00:28:39,440
Speaker 1: superposition just means it has a chance to be in

537
00:28:39,560 --> 00:28:42,160
Speaker 1: more than one state, So rather than being a zero

538
00:28:42,520 --> 00:28:44,400
Speaker 1: or a one, it can be like, well, this one

539
00:28:44,440 --> 00:28:46,320
Speaker 1: has a thirty percent chance of being a zero and

540
00:28:46,400 --> 00:28:48,719
Speaker 1: a seventy percent chance of being a one. That one

541
00:28:48,800 --> 00:28:51,200
Speaker 1: over there has a ninety percent chance of being a

542
00:28:51,320 --> 00:28:53,240
Speaker 1: zero and a ten percent chance of being a one.

543
00:28:53,800 --> 00:28:56,440
Speaker 1: So a superposition just means two things laid on top

544
00:28:56,480 --> 00:28:59,280
Speaker 1: of each other doesn't have to be zero or one.

545
00:28:59,640 --> 00:29:03,080
Speaker 1: It can be some probability of zero or one. And

546
00:29:03,200 --> 00:29:06,040
Speaker 1: the fact that it can maintain these superpositions lets it

547
00:29:06,160 --> 00:29:10,040
Speaker 1: do something that classical bits can't do, which is interfere

548
00:29:10,480 --> 00:29:13,040
Speaker 1: If you've heard of the double slit experiment, this is

549
00:29:13,160 --> 00:29:16,000
Speaker 1: like a beam of photons that go through two slits,

550
00:29:16,320 --> 00:29:18,680
Speaker 1: and maybe the photons come from one slit, and maybe

551
00:29:18,680 --> 00:29:21,800
Speaker 1: the photons go through another slit, and the possibility that

552
00:29:21,880 --> 00:29:25,320
Speaker 1: they've gone through both slits interferes with each other, creating

553
00:29:25,360 --> 00:29:28,320
Speaker 1: this interference pattern on the final screen. And it's the

554
00:29:28,440 --> 00:29:31,080
Speaker 1: fact that the photons can be in a superposition of

555
00:29:31,120 --> 00:29:33,880
Speaker 1: those states, that they can be maybe through slit one

556
00:29:33,960 --> 00:29:36,960
Speaker 1: and maybe through slit two. Those possibilities are the things

557
00:29:37,080 --> 00:29:40,960
Speaker 1: doing the interfering. So that interference is very important part

558
00:29:41,280 --> 00:29:44,520
Speaker 1: of what quantum systems can do and what classical systems

559
00:29:44,600 --> 00:29:46,760
Speaker 1: cannot do because the classical system like if you throw

560
00:29:46,760 --> 00:29:49,560
Speaker 1: a base ball through a double slit experiment, it either

561
00:29:49,600 --> 00:29:51,480
Speaker 1: went through the left one or through the right one.

562
00:29:51,640 --> 00:29:55,000
Speaker 1: There's no superposition and there's no interference. Now these are

563
00:29:55,080 --> 00:29:57,560
Speaker 1: quantum systems that can do this weird thing of having

564
00:29:57,720 --> 00:30:01,440
Speaker 1: multiple possibilities at the same time. But then when quantum

565
00:30:01,480 --> 00:30:05,360
Speaker 1: systems interact with classical systems, when you ask the quantum system, hey,

566
00:30:05,880 --> 00:30:08,240
Speaker 1: what is the value of this bit? Because eventually you

567
00:30:08,360 --> 00:30:10,280
Speaker 1: want to know the answer from your computer, right you

568
00:30:10,360 --> 00:30:12,720
Speaker 1: have to make a measurement, you have to do something,

569
00:30:12,760 --> 00:30:15,760
Speaker 1: you interact with it. That's when the universe picks one

570
00:30:15,840 --> 00:30:18,400
Speaker 1: of the options. So there's a spread of possible outcomes

571
00:30:18,440 --> 00:30:21,920
Speaker 1: for any quantum interaction. There's a superposition that describes the

572
00:30:22,000 --> 00:30:25,400
Speaker 1: various possibilities, and measurement is the thing that collapses it

573
00:30:25,680 --> 00:30:29,000
Speaker 1: that makes the universe pick one of those outcomes. So

574
00:30:29,120 --> 00:30:31,560
Speaker 1: this is the way the universe works in the microscopic scale,

575
00:30:31,560 --> 00:30:34,160
Speaker 1: which is weird and amazing and very different from our

576
00:30:34,280 --> 00:30:37,880
Speaker 1: experience and the macroscopic scale, and it opens the door

577
00:30:37,960 --> 00:30:41,920
Speaker 1: to doing different kinds of computation. Remember a computer, it's

578
00:30:42,000 --> 00:30:45,200
Speaker 1: just a physical system we've arranged to calculate something we're

579
00:30:45,240 --> 00:30:47,840
Speaker 1: interested in. We take advantage of the way that physical

580
00:30:47,880 --> 00:30:50,800
Speaker 1: system works to represent some kind of calculation and hope

581
00:30:50,800 --> 00:30:52,800
Speaker 1: that that computer that we've built is good at that

582
00:30:52,960 --> 00:30:56,520
Speaker 1: kind of calculation. And because quantum computers work with these

583
00:30:56,640 --> 00:30:59,280
Speaker 1: very different rules, it means they can do different kinds

584
00:30:59,320 --> 00:31:02,200
Speaker 1: of calculation, and they can do different calculations quickly, and

585
00:31:02,240 --> 00:31:05,840
Speaker 1: they have different strengths and weaknesses than classical computers. So

586
00:31:05,960 --> 00:31:07,440
Speaker 1: here's Scott telling us more about that.

587
00:31:08,080 --> 00:31:12,240
Speaker 4: Let's now imagine a computer that operates by these principles

588
00:31:12,240 --> 00:31:15,560
Speaker 4: of quantum mechanics, we'll call it a quantum computer, so.

589
00:31:15,840 --> 00:31:16,920
Speaker 5: A classical computer.

590
00:31:17,360 --> 00:31:21,120
Speaker 4: Typically, for simplicity, we like to imagine it as having

591
00:31:21,200 --> 00:31:23,840
Speaker 4: a state that's built up out of bits out of

592
00:31:23,880 --> 00:31:26,760
Speaker 4: you know, zeros are ones, right, and if there's one

593
00:31:26,800 --> 00:31:30,360
Speaker 4: thousand bits, then there's two to one thousand power possible

594
00:31:30,440 --> 00:31:34,880
Speaker 4: configurations of that computer's memory, okay. But now with a

595
00:31:34,960 --> 00:31:39,320
Speaker 4: quantum computer, there's going to be vastly more configurations than that, okay,

596
00:31:39,360 --> 00:31:43,040
Speaker 4: because if I have even one quantum bit or you know,

597
00:31:43,120 --> 00:31:46,360
Speaker 4: what we call a cube bit, right, then it can

598
00:31:46,440 --> 00:31:49,920
Speaker 4: have some amplitude for being zero and some amplitude for

599
00:31:50,040 --> 00:31:53,160
Speaker 4: being one at the same time. So it can be

600
00:31:53,280 --> 00:31:56,280
Speaker 4: in what we call a superposition of the zero state

601
00:31:56,400 --> 00:31:57,920
Speaker 4: and the one state, you know, at.

602
00:31:57,880 --> 00:31:59,560
Speaker 5: Least before we look at it.

603
00:32:00,160 --> 00:32:02,560
Speaker 4: Once we make a measurement, then we'll force it to

604
00:32:02,720 --> 00:32:05,040
Speaker 4: snap to either zero or one, and then it will

605
00:32:05,560 --> 00:32:07,800
Speaker 4: probabilistically collapse to one or the other.

606
00:32:08,120 --> 00:32:10,960
Speaker 5: But before we look it can be in this superposition state.

607
00:32:11,560 --> 00:32:13,960
Speaker 5: And now if I have let's.

608
00:32:13,680 --> 00:32:17,400
Speaker 4: Say three cubits, okay, then it's not enough to give

609
00:32:17,480 --> 00:32:20,920
Speaker 4: amplitudes for each cubit separately from the others. Right, The

610
00:32:21,040 --> 00:32:24,600
Speaker 4: rules of quantum mechanics are unequivocal. I have to give

611
00:32:24,680 --> 00:32:27,720
Speaker 4: an amplitude that the three bits are zero zero zero.

612
00:32:28,240 --> 00:32:30,160
Speaker 4: I have to give an amplitude that there are zero

613
00:32:30,320 --> 00:32:32,440
Speaker 4: zero one. I have to give an amplitude that they're

614
00:32:32,520 --> 00:32:35,040
Speaker 4: zero one zero, you know, and so on, so I

615
00:32:35,120 --> 00:32:36,640
Speaker 4: have to give eight amplitudes.

616
00:32:37,200 --> 00:32:40,400
Speaker 1: So it's sort of more information dense because the amount

617
00:32:40,400 --> 00:32:43,560
Speaker 1: of information grows exponentially. But why does that allow us

618
00:32:43,640 --> 00:32:46,360
Speaker 1: to do different kinds of computation? Why does that allow

619
00:32:46,400 --> 00:32:50,160
Speaker 1: us to solve different kinds of problems efficiently than classical computers.

620
00:32:50,360 --> 00:32:52,240
Speaker 4: Yes, I mean, of course that's the point, and that's

621
00:32:52,280 --> 00:32:54,680
Speaker 4: where this is ultimately headed. But we have to be

622
00:32:54,840 --> 00:32:58,000
Speaker 4: very careful about it, because if you take these three

623
00:32:58,120 --> 00:32:59,600
Speaker 4: cubits and you measure.

624
00:32:59,320 --> 00:33:01,560
Speaker 5: Them, don't see those eight numbers.

625
00:33:01,840 --> 00:33:06,560
Speaker 4: Now, the cubits collapse and you just see three bits each,

626
00:33:06,720 --> 00:33:09,880
Speaker 4: you know, with some probability. Right now, if I have

627
00:33:10,280 --> 00:33:13,880
Speaker 4: one thousand cubits, right, then that's two to the thousand

628
00:33:14,000 --> 00:33:17,600
Speaker 4: power amplitudes to keep track of their state, right, which is,

629
00:33:17,680 --> 00:33:19,240
Speaker 4: you know, more than you could write down in the

630
00:33:19,320 --> 00:33:22,280
Speaker 4: whole observable universe. Okay, so there seems to be that

631
00:33:22,440 --> 00:33:26,600
Speaker 4: this exponentiality, you know, beneath the surface. And this is

632
00:33:26,720 --> 00:33:30,760
Speaker 4: certainly a problem if you wanted to simulate quantum mechanics

633
00:33:30,920 --> 00:33:35,000
Speaker 4: on a conventional computer. Okay. And actually chemists and physicists

634
00:33:35,040 --> 00:33:38,160
Speaker 4: have known that for generations, right. They're like, once they

635
00:33:38,240 --> 00:33:41,880
Speaker 4: started trying to apply the Schrodinger equation to you know,

636
00:33:42,040 --> 00:33:46,120
Speaker 4: calculate the behavior of even quite simple molecules, right, they

637
00:33:46,200 --> 00:33:49,200
Speaker 4: have to write down a wave function that has like

638
00:33:49,680 --> 00:33:53,640
Speaker 4: more and more dimensions, you know, the more electrons you add, right,

639
00:33:53,800 --> 00:33:57,000
Speaker 4: and this you very quickly get the problems that would

640
00:33:57,080 --> 00:34:00,480
Speaker 4: tax you know, the fastest supercomputers of today. You know,

641
00:34:00,600 --> 00:34:04,200
Speaker 4: let alone of the nineteen fifties when they started doing this, right,

642
00:34:04,760 --> 00:34:07,880
Speaker 4: And so you know, chemists and physicists invented all sorts

643
00:34:07,960 --> 00:34:13,080
Speaker 4: of hacks and approximation methods for dealing with these exponentially

644
00:34:13,280 --> 00:34:16,920
Speaker 4: large wave functions. Okay, But it was not until the

645
00:34:17,080 --> 00:34:21,840
Speaker 4: early eighties that a few physicists like Feineman and Deutsch,

646
00:34:22,239 --> 00:34:25,200
Speaker 4: you know, started saying, well, if nature is giving us

647
00:34:25,320 --> 00:34:28,320
Speaker 4: this computational lemon, you know, it is making it so

648
00:34:28,480 --> 00:34:31,920
Speaker 4: hard for us to simulate atomic physics on computers.

649
00:34:32,160 --> 00:34:34,520
Speaker 5: Then why don't we make lemonade? That is, you know,

650
00:34:34,600 --> 00:34:35,680
Speaker 5: why don't we build.

651
00:34:35,440 --> 00:34:40,520
Speaker 4: A computer that itself would exploit quantum mechanical principles, you

652
00:34:40,600 --> 00:34:42,240
Speaker 4: know what they called a quantum computer.

653
00:34:42,719 --> 00:34:44,520
Speaker 5: And then what would that be good for?

654
00:34:44,960 --> 00:34:47,560
Speaker 4: Well, if nothing else, it would be good for simulating

655
00:34:47,640 --> 00:34:50,680
Speaker 4: quantum mechanics itself, right, And that was sort of the

656
00:34:50,800 --> 00:34:55,080
Speaker 4: original application that they had in mind, And forty years later,

657
00:34:55,239 --> 00:34:58,080
Speaker 4: I think that that's still, honestly, you know, the most

658
00:34:58,120 --> 00:35:01,680
Speaker 4: important application economic that we know. A bet that's the

659
00:35:01,760 --> 00:35:02,960
Speaker 4: truth of the matter, right.

660
00:35:03,360 --> 00:35:04,239
Speaker 5: Anyone who is.

661
00:35:04,320 --> 00:35:08,680
Speaker 4: Trying to design better batteries or better solar cells, or

662
00:35:09,040 --> 00:35:14,520
Speaker 4: high temperature superconductors, or better chemical reactions for making fertilizer

663
00:35:15,040 --> 00:35:18,719
Speaker 4: or better drugs that you know buind a receptor in

664
00:35:18,800 --> 00:35:21,600
Speaker 4: a certain way, right, they're basically dealing with a many

665
00:35:21,680 --> 00:35:25,480
Speaker 4: body quantum mechanics problem. And these problems can be incredibly

666
00:35:25,600 --> 00:35:31,279
Speaker 4: hard for classical computers for reasons that you know, ultimately

667
00:35:31,719 --> 00:35:35,400
Speaker 4: come from the exponentiality of the wave function, right, and

668
00:35:35,680 --> 00:35:38,960
Speaker 4: a quantum computer could potentially help with any of that.

669
00:35:39,920 --> 00:35:43,560
Speaker 1: Scott is pointing out a really crucial feature of quantum systems.

670
00:35:44,000 --> 00:35:46,080
Speaker 1: Not only do they have these cubits that can be

671
00:35:46,200 --> 00:35:49,279
Speaker 1: in superposition, but the cubits can be entangled with each other,

672
00:35:49,320 --> 00:35:52,040
Speaker 1: which means the value in one bit can be linked

673
00:35:52,120 --> 00:35:54,680
Speaker 1: to the value in another bit, and that's what makes

674
00:35:54,719 --> 00:35:58,080
Speaker 1: them much more information dense than classical computers. It's like,

675
00:35:58,160 --> 00:36:01,160
Speaker 1: instead of having three independent axes where you can just

676
00:36:01,239 --> 00:36:03,400
Speaker 1: pick a number along the access, you have three pieces

677
00:36:03,440 --> 00:36:06,800
Speaker 1: of information. You semble those into a three dimensional space,

678
00:36:07,239 --> 00:36:09,600
Speaker 1: so now you have a three D volume of information.

679
00:36:09,960 --> 00:36:13,400
Speaker 1: So they're capable of storing information much more densely because

680
00:36:13,400 --> 00:36:17,280
Speaker 1: of these connections between the bits, which creates this information space,

681
00:36:18,040 --> 00:36:21,279
Speaker 1: and this makes it very hard for classical computers to

682
00:36:21,480 --> 00:36:25,120
Speaker 1: simulate a quantum system. It takes a lot of normal bits,

683
00:36:25,200 --> 00:36:29,120
Speaker 1: classical zero one bits to calculate what a quantum system

684
00:36:29,160 --> 00:36:31,840
Speaker 1: will do or what a cbe bit will do. So

685
00:36:32,000 --> 00:36:34,880
Speaker 1: the first thing that quantum computers could be good for

686
00:36:35,200 --> 00:36:37,239
Speaker 1: is to just describe quantum systems. It's kind of a

687
00:36:37,320 --> 00:36:40,959
Speaker 1: natural application. You know, the quantum computer follows similar rules

688
00:36:41,040 --> 00:36:43,440
Speaker 1: to the quantum system, and so it's natural to describe

689
00:36:43,480 --> 00:36:45,560
Speaker 1: it in that way, the same way like the baseball

690
00:36:45,640 --> 00:36:47,560
Speaker 1: follows the rules of the baseball. So it's a good

691
00:36:47,600 --> 00:36:50,480
Speaker 1: way to calculate what a baseball will do. But of

692
00:36:50,560 --> 00:36:53,319
Speaker 1: course we wonder, like, is that all quantum computers can

693
00:36:53,400 --> 00:36:58,320
Speaker 1: do simulate some nerdy quantum experiment or are quantum computers

694
00:36:58,360 --> 00:37:02,440
Speaker 1: also good at doing other things? Here's Scott telling us

695
00:37:02,440 --> 00:37:02,920
Speaker 1: all about it.

696
00:37:03,400 --> 00:37:04,840
Speaker 5: That was the original promise.

697
00:37:05,320 --> 00:37:07,360
Speaker 4: But you know, as long as that was sort of

698
00:37:07,880 --> 00:37:10,560
Speaker 4: the only promise, I think, you know, this remained very

699
00:37:10,680 --> 00:37:16,440
Speaker 4: much a niche interest of some weird physicists pursuing this idea.

700
00:37:16,560 --> 00:37:18,840
Speaker 5: And you know, the eighties, the early nineties.

701
00:37:19,280 --> 00:37:22,759
Speaker 4: Now, the big discovery that put quantum computing on the

702
00:37:22,880 --> 00:37:25,080
Speaker 4: map for you know, most of the rest of the

703
00:37:25,160 --> 00:37:29,239
Speaker 4: world was that a quantum computer can sometimes also help

704
00:37:30,400 --> 00:37:33,880
Speaker 4: to get exponential speed ups, even for problems that have

705
00:37:34,040 --> 00:37:37,160
Speaker 4: nothing to do with quantum mechanics, at least for a

706
00:37:37,280 --> 00:37:39,560
Speaker 4: few very specific such problems.

707
00:37:40,120 --> 00:37:42,640
Speaker 1: Can we predict these kinds of problems in advance?

708
00:37:43,000 --> 00:37:45,239
Speaker 4: Welcome to what my colleagues and I have been trying

709
00:37:45,280 --> 00:37:46,680
Speaker 4: to do for the last thirty years.

710
00:37:46,880 --> 00:37:48,960
Speaker 5: Yeah, I mean, for the whole history of this field.

711
00:37:49,080 --> 00:37:51,520
Speaker 4: We are trying to figure out what is the border

712
00:37:51,680 --> 00:37:55,200
Speaker 4: between what is efficiently solvable by a quantum computer and

713
00:37:55,280 --> 00:37:57,560
Speaker 4: what isn't and we know a lot about it, but

714
00:37:57,719 --> 00:37:58,319
Speaker 4: you know, there is.

715
00:37:58,320 --> 00:37:59,920
Speaker 5: A great deal that we still don't know.

716
00:38:00,560 --> 00:38:04,759
Speaker 4: The big discovery that sort of started quantum computing as

717
00:38:04,800 --> 00:38:07,279
Speaker 4: a field, I would say, you know, as opposed to

718
00:38:07,400 --> 00:38:10,600
Speaker 4: just an idea, came in nineteen ninety four, Okay, and

719
00:38:10,680 --> 00:38:14,040
Speaker 4: that was when Peter Shore, who was a mathematician then

720
00:38:14,120 --> 00:38:18,400
Speaker 4: at Belle Ebs, discovered that there is a fast quantum

721
00:38:18,520 --> 00:38:20,560
Speaker 4: algorithm for factoring numbers.

722
00:38:21,120 --> 00:38:23,600
Speaker 5: Okay, So he discovered that the factoring.

723
00:38:23,200 --> 00:38:28,120
Speaker 4: Problem, the problem of factoring a huge composite number into primes,

724
00:38:28,719 --> 00:38:33,640
Speaker 4: and some various closely related problems of central importance in

725
00:38:33,800 --> 00:38:39,680
Speaker 4: modern cryptography are all solvable on a quantum computer using

726
00:38:39,760 --> 00:38:43,279
Speaker 4: a number of steps that grows like the size of

727
00:38:43,360 --> 00:38:47,040
Speaker 4: the number. You know that you're trying to factor squared maybe,

728
00:38:47,400 --> 00:38:49,480
Speaker 4: but not exponentially with the size.

729
00:38:49,280 --> 00:38:49,800
Speaker 2: Of the number.

730
00:38:50,200 --> 00:38:52,680
Speaker 1: So this is a big deal because, as you're saying,

731
00:38:53,040 --> 00:38:55,080
Speaker 1: you know, we can use billiard balls to calculate how

732
00:38:55,120 --> 00:38:57,440
Speaker 1: billiard balls move, and we can use quantum systems to

733
00:38:57,480 --> 00:39:00,600
Speaker 1: simulate quantum systems. But now we're using a quotum system

734
00:39:00,840 --> 00:39:03,279
Speaker 1: to describe something that's fundamentally not quantum. So it gives

735
00:39:03,320 --> 00:39:05,319
Speaker 1: us a clue that like, maybe we can open up

736
00:39:05,320 --> 00:39:06,800
Speaker 1: a whole new category of problems.

737
00:39:07,000 --> 00:39:10,400
Speaker 4: It is totally not obvious a priori that a quantum

738
00:39:10,440 --> 00:39:12,800
Speaker 4: computer should help you for factoring numbers.

739
00:39:13,160 --> 00:39:15,480
Speaker 5: You know, what does that have to do with quantum mechanics?

740
00:39:15,960 --> 00:39:20,120
Speaker 4: Right? And of course this problem is hugely important because,

741
00:39:20,280 --> 00:39:22,920
Speaker 4: for better or worse, we base the whole security of

742
00:39:23,040 --> 00:39:27,439
Speaker 4: the modern Internet on the belief that factoring is hard. Okay,

743
00:39:27,560 --> 00:39:30,000
Speaker 4: What Sure was saying is that if and when someone

744
00:39:30,120 --> 00:39:34,759
Speaker 4: builds a large quantum computer, a scalable quantum computer with

745
00:39:34,920 --> 00:39:38,320
Speaker 4: you know, thousands or millions of cubits, then that is

746
00:39:38,400 --> 00:39:41,440
Speaker 4: no longer true. Okay, then you can break all of

747
00:39:41,520 --> 00:39:44,160
Speaker 4: the encryption that we use to protect the Internet.

748
00:39:44,640 --> 00:39:47,160
Speaker 5: So a bunch of things happened, you know after that.

749
00:39:47,360 --> 00:39:49,719
Speaker 4: You know, one was people you know kind of like

750
00:39:49,880 --> 00:39:52,960
Speaker 4: with the story of Rumpelstiltskin, Right, It's like, if you

751
00:39:53,080 --> 00:39:56,080
Speaker 4: can spin this much straw into gold, and then why

752
00:39:56,160 --> 00:39:59,960
Speaker 4: not more? And people said, well, maybe all of the exponential,

753
00:40:00,000 --> 00:40:03,120
Speaker 4: really hard problems that we're you know, dealing with, maybe

754
00:40:03,200 --> 00:40:05,120
Speaker 4: quantum computers can solve all of them.

755
00:40:05,320 --> 00:40:08,600
Speaker 1: Is there any way to intuitively understand the idea here?

756
00:40:08,760 --> 00:40:11,719
Speaker 1: Like what it is about quantum computing that makes this

757
00:40:12,120 --> 00:40:13,320
Speaker 1: problem easier to do.

758
00:40:13,800 --> 00:40:16,239
Speaker 4: I teach a whole undergrad course where you know, at

759
00:40:16,320 --> 00:40:18,040
Speaker 4: the end of it, I hope that people will have

760
00:40:18,200 --> 00:40:19,480
Speaker 4: the intuition for these things.

761
00:40:19,560 --> 00:40:19,640
Speaker 8: Right.

762
00:40:19,680 --> 00:40:21,680
Speaker 4: But like, if there were a one sentence way to

763
00:40:21,760 --> 00:40:24,400
Speaker 4: say the intuition, then you wouldn't have needed Shore and

764
00:40:24,480 --> 00:40:27,600
Speaker 4: Grover to discover these things, right, you know, it could

765
00:40:27,600 --> 00:40:29,080
Speaker 4: have been obvious from the beginning.

766
00:40:29,280 --> 00:40:32,319
Speaker 5: Right. But let me say this, right, so, you know.

767
00:40:32,400 --> 00:40:36,320
Speaker 4: What almost every popular article about quantum computing wants to

768
00:40:36,440 --> 00:40:41,520
Speaker 4: say is something that sounds really appealing and is totally wrong. Okay.

769
00:40:42,080 --> 00:40:44,680
Speaker 4: In fact, Zach Kelly's husband and I made a whole

770
00:40:44,800 --> 00:40:47,279
Speaker 4: cartoon about exactly this eight years ago.

771
00:40:47,520 --> 00:40:49,839
Speaker 1: So throw cold water on some clickbait for us. What's

772
00:40:49,920 --> 00:40:52,440
Speaker 1: wrong about quantum computing descriptions?

773
00:40:52,840 --> 00:40:56,160
Speaker 4: What almost every popular writer has wanted to say is

774
00:40:56,239 --> 00:41:01,000
Speaker 4: that a quantum computer just tries every possible solution in parallel.

775
00:41:01,200 --> 00:41:04,600
Speaker 4: You know, it tries each one in a different parallel universe,

776
00:41:04,800 --> 00:41:07,200
Speaker 4: or you know, all of them in superposition or whatever,

777
00:41:07,280 --> 00:41:11,120
Speaker 4: and then somehow magically the best one gets picked. Right.

778
00:41:11,560 --> 00:41:14,720
Speaker 4: If that were how it worked, then quantum computers would solve,

779
00:41:14,840 --> 00:41:19,319
Speaker 4: not only factoring, but also np complete problems. Right, they

780
00:41:19,360 --> 00:41:23,720
Speaker 4: would break not only the cryptosystems that currently protect the Internet,

781
00:41:23,840 --> 00:41:27,360
Speaker 4: but they would break all other possible cryptosystems you know

782
00:41:28,000 --> 00:41:30,360
Speaker 4: that are based on hard problems. Okay, but that is

783
00:41:30,480 --> 00:41:33,000
Speaker 4: not what we believe that quantum computers can do, right.

784
00:41:33,120 --> 00:41:35,279
Speaker 4: We believe that they're more limited than that. So the

785
00:41:35,400 --> 00:41:37,400
Speaker 4: question is why are they more limited? Okay?

786
00:41:37,719 --> 00:41:41,280
Speaker 5: And it all has to do with the restrictions of measurement.

787
00:41:41,760 --> 00:41:41,880
Speaker 2: Right.

788
00:41:42,280 --> 00:41:46,400
Speaker 4: It's true that with a quantum computer you can create

789
00:41:46,760 --> 00:41:50,719
Speaker 4: an equal superposition over all the possible solutions to your

790
00:41:50,800 --> 00:41:54,080
Speaker 4: hard problem. That's even an easy thing to do if

791
00:41:54,120 --> 00:41:57,760
Speaker 4: you have a quantum computer, right, like create a superposition

792
00:41:58,239 --> 00:42:00,480
Speaker 4: or each of these two to the thousand and power

793
00:42:00,640 --> 00:42:04,200
Speaker 4: possible solutions has some amplitude. You know, that's just like

794
00:42:04,719 --> 00:42:07,960
Speaker 4: very simple, it's done, Okay. The trouble is for a

795
00:42:08,040 --> 00:42:10,800
Speaker 4: computer to be useful, at some point you have to

796
00:42:10,840 --> 00:42:13,560
Speaker 4: look at it. You have to measure, you have to

797
00:42:13,680 --> 00:42:17,000
Speaker 4: get an output. You know, you have to read something out. Okay.

798
00:42:17,080 --> 00:42:19,600
Speaker 4: And if you took an equal superposition over all the

799
00:42:19,680 --> 00:42:23,000
Speaker 4: answers and you just measured it, not having done anything else,

800
00:42:23,440 --> 00:42:26,040
Speaker 4: then the rules of quantum mechanics are very clear on

801
00:42:26,160 --> 00:42:28,960
Speaker 4: what you're going to see. It's a completely random answer.

802
00:42:29,440 --> 00:42:32,120
Speaker 4: And if you had just wanted a completely random answer,

803
00:42:32,400 --> 00:42:34,239
Speaker 4: then you could have just flipped a coin a bunch

804
00:42:34,280 --> 00:42:36,879
Speaker 4: of times, or just you know, use a random number

805
00:42:37,040 --> 00:42:40,160
Speaker 4: generator that's inside your classical computer. Right, You didn't need

806
00:42:40,280 --> 00:42:42,520
Speaker 4: to spend all these billions of dollars to build.

807
00:42:42,360 --> 00:42:43,160
Speaker 5: A quantum computer.

808
00:42:43,680 --> 00:42:47,879
Speaker 4: Right, So the only hope of getting an advantage from

809
00:42:47,960 --> 00:42:52,400
Speaker 4: a quantum computer is to exploit the way that these amplitudes,

810
00:42:52,920 --> 00:42:59,400
Speaker 4: you know, being complex numbers, work differently from conventional probabilities. Okay,

811
00:42:59,520 --> 00:43:04,320
Speaker 4: And the central thing that amplitudes can do that probabilities

812
00:43:04,400 --> 00:43:07,399
Speaker 4: cannot do is that they can interfere with each other.

813
00:43:07,920 --> 00:43:10,880
Speaker 4: They can cancel each other out. And so with a

814
00:43:10,960 --> 00:43:15,520
Speaker 4: quantum computer in particular, the idea with every algorithm for

815
00:43:15,640 --> 00:43:19,080
Speaker 4: a quantum computer is that you are trying to choreograph

816
00:43:19,280 --> 00:43:22,320
Speaker 4: things in such a way that for each wrong answer,

817
00:43:22,560 --> 00:43:25,239
Speaker 4: because each answer that you don't want to see, some

818
00:43:25,560 --> 00:43:29,480
Speaker 4: contributions to its amplitude are positive and others are negative,

819
00:43:29,800 --> 00:43:31,440
Speaker 4: so they're canceling each other out.

820
00:43:32,040 --> 00:43:32,799
Speaker 5: Whereas for the.

821
00:43:32,880 --> 00:43:35,880
Speaker 4: Right answer, the answer you do one you want all

822
00:43:35,960 --> 00:43:40,320
Speaker 4: the contributions to its amplitude to reinforce each other, okay,

823
00:43:40,440 --> 00:43:44,600
Speaker 4: to add up constructively. If you can arrange that, then

824
00:43:44,640 --> 00:43:46,800
Speaker 4: when you look, you're going to see the right answer

825
00:43:47,160 --> 00:43:48,600
Speaker 4: with a large probability.

826
00:43:49,160 --> 00:43:50,319
Speaker 5: That's the name of the game.

827
00:43:50,800 --> 00:43:53,640
Speaker 4: Now. The hard part is you have to do all

828
00:43:53,719 --> 00:43:56,480
Speaker 4: of that even though you yourself don't know in advance

829
00:43:56,560 --> 00:43:57,920
Speaker 4: which answer is the right one.

830
00:43:58,280 --> 00:44:00,320
Speaker 5: You know, if you already knew, what would be the point?

831
00:44:00,840 --> 00:44:03,680
Speaker 4: Right, And you have to do all of this faster

832
00:44:04,120 --> 00:44:07,920
Speaker 4: than even the cleverest classical algorithm could do the same thing.

833
00:44:08,080 --> 00:44:09,520
Speaker 5: Otherwise what would be the point?

834
00:44:09,880 --> 00:44:13,760
Speaker 4: Okay, so nature is giving us this really really bizarre

835
00:44:13,920 --> 00:44:15,920
Speaker 4: hammer and a priori.

836
00:44:16,040 --> 00:44:17,520
Speaker 5: It's not obvious whether.

837
00:44:17,320 --> 00:44:20,160
Speaker 4: There's any nails that that hammer can hit, you know,

838
00:44:20,320 --> 00:44:24,640
Speaker 4: other than just the obvious one of simulating quantum physics itself, right,

839
00:44:24,680 --> 00:44:26,640
Speaker 4: And it really it took more than a decade for

840
00:44:26,719 --> 00:44:30,640
Speaker 4: people to discover those nails and factoring the problem that

841
00:44:30,760 --> 00:44:33,960
Speaker 4: Peter Shore designed his algorithm for. That was the first

842
00:44:34,280 --> 00:44:37,719
Speaker 4: big example, and some people hope that that would be

843
00:44:37,880 --> 00:44:42,120
Speaker 4: followed by a flood of other examples, and you know, unfortunately,

844
00:44:42,160 --> 00:44:45,319
Speaker 4: you know, thirty years later, factoring remains one of our

845
00:44:45,400 --> 00:44:46,640
Speaker 4: pre eminent examples.

846
00:44:47,320 --> 00:44:49,919
Speaker 1: So I have a crazy question for you. You're telling

847
00:44:50,000 --> 00:44:53,879
Speaker 1: me that quantum computers work by maintaining all of these

848
00:44:54,000 --> 00:44:57,480
Speaker 1: different amplitudes and the superpositions, but that we don't have

849
00:44:57,680 --> 00:44:59,839
Speaker 1: access to all the superpositions because we have to take

850
00:44:59,880 --> 00:45:02,200
Speaker 1: the measurement. So we have to play clever games with

851
00:45:02,360 --> 00:45:05,560
Speaker 1: interference so that we can use this system to do

852
00:45:05,680 --> 00:45:08,440
Speaker 1: something useful. But we don't have access to the superpositions

853
00:45:08,480 --> 00:45:12,000
Speaker 1: because of the measurement, only because we are classical objects

854
00:45:12,080 --> 00:45:15,800
Speaker 1: and classical interactions with quantum systems collapse the measurement. What

855
00:45:16,000 --> 00:45:19,960
Speaker 1: if we were tiny, we were microscopic, we were quantum,

856
00:45:20,560 --> 00:45:23,440
Speaker 1: could then we use quantum systems in a way that

857
00:45:23,560 --> 00:45:26,880
Speaker 1: didn't collapse all their wave functions and access all of

858
00:45:27,040 --> 00:45:27,960
Speaker 1: those amplitudes.

859
00:45:28,480 --> 00:45:30,200
Speaker 4: So I hate to break it to you, us being

860
00:45:30,280 --> 00:45:33,560
Speaker 4: microscopic wouldn't help. Right, You can be as tiny as

861
00:45:33,600 --> 00:45:36,600
Speaker 4: you like, But if you interact with a quantum system

862
00:45:36,920 --> 00:45:40,040
Speaker 4: in a way that carries away the information about you know,

863
00:45:40,160 --> 00:45:43,000
Speaker 4: which branch of the superposition we're we in, then that

864
00:45:43,200 --> 00:45:46,560
Speaker 4: has exactly the same effect of collapsing the state. Right,

865
00:45:46,640 --> 00:45:50,400
Speaker 4: So it's not our physical bigness that's the issue. It's that,

866
00:45:50,560 --> 00:45:55,600
Speaker 4: you know, sort of we want an answer in our universe, right,

867
00:45:55,840 --> 00:45:58,279
Speaker 4: what we can say, You know, there are people who

868
00:45:58,400 --> 00:46:02,320
Speaker 4: are very gung ho about the interpretation of quantum mechanics,

869
00:46:02,360 --> 00:46:05,960
Speaker 4: where they would say, collapse is not real, right, collapse

870
00:46:06,080 --> 00:46:09,800
Speaker 4: is just a figment of our limited perspective. Right. Really,

871
00:46:09,880 --> 00:46:12,879
Speaker 4: what's going on is it's just the Schrodinger equation all

872
00:46:12,960 --> 00:46:15,920
Speaker 4: the way. And so they would say that whenever you know,

873
00:46:16,080 --> 00:46:19,600
Speaker 4: you measure a quantum computer that's in a superposition, Actually

874
00:46:19,640 --> 00:46:23,000
Speaker 4: the whole universe then splits into all these different branches,

875
00:46:23,480 --> 00:46:25,319
Speaker 4: and each branch is equally real.

876
00:46:25,840 --> 00:46:28,520
Speaker 5: We have an experience of one of them where.

877
00:46:28,320 --> 00:46:31,400
Speaker 4: We perceive some answer. So a many worlder, that's what

878
00:46:31,600 --> 00:46:34,560
Speaker 4: these people are called, right, a Mani worlder would say

879
00:46:34,960 --> 00:46:38,760
Speaker 4: that there is some branch of the universal wave function

880
00:46:39,000 --> 00:46:42,000
Speaker 4: in which you do get the right answer right, in

881
00:46:42,120 --> 00:46:45,719
Speaker 4: which you try all the possible answers in superposition. You know,

882
00:46:45,800 --> 00:46:48,919
Speaker 4: they would say, well, there's some branch where you get

883
00:46:49,000 --> 00:46:52,480
Speaker 4: lucky and you measure the right answer, right. And they love,

884
00:46:52,920 --> 00:46:56,440
Speaker 4: you know, all sorts of crazy thought experiments, like you know,

885
00:46:56,600 --> 00:46:58,480
Speaker 4: quantum suicide, right, Like why not.

886
00:46:58,960 --> 00:47:00,640
Speaker 5: Use a quantum random number?

887
00:47:00,760 --> 00:47:04,760
Speaker 4: Generator to pick a lottery ticket and then just decide

888
00:47:04,840 --> 00:47:07,320
Speaker 4: to kill yourself if it doesn't end up being winning,

889
00:47:07,680 --> 00:47:10,040
Speaker 4: and then in all the branches of the wave function

890
00:47:10,200 --> 00:47:11,040
Speaker 4: where you still.

891
00:47:10,880 --> 00:47:13,320
Speaker 5: Exist, you know you'll have won the lottery.

892
00:47:13,719 --> 00:47:17,560
Speaker 4: Now, I do not recommend that any listeners try that, Okay.

893
00:47:18,440 --> 00:47:22,040
Speaker 4: I don't think there's any principle of reason that says,

894
00:47:22,120 --> 00:47:24,920
Speaker 4: you know, you get to condition on being in a

895
00:47:25,000 --> 00:47:26,960
Speaker 4: branch of the wave function where you're alive.

896
00:47:27,880 --> 00:47:30,479
Speaker 1: Yeah, it sounds like a cool premise for a streaming show,

897
00:47:30,560 --> 00:47:31,680
Speaker 1: but not a way to live your life.

898
00:47:31,920 --> 00:47:33,800
Speaker 4: That is the one thing that I can say in

899
00:47:34,080 --> 00:47:36,560
Speaker 4: the direction of what you were hoping for, and it's

900
00:47:36,640 --> 00:47:38,000
Speaker 4: not very useful, I'm afraid.

901
00:47:54,440 --> 00:47:54,719
Speaker 2: All right.

902
00:47:54,760 --> 00:47:56,759
Speaker 1: So you're sort of famous for, you know, throwing cold

903
00:47:56,840 --> 00:48:01,360
Speaker 1: water on mis explanations of quantum computing and maybe even overhype.

904
00:48:01,800 --> 00:48:04,000
Speaker 1: Let me flip that around and ask you what is

905
00:48:04,160 --> 00:48:07,160
Speaker 1: the most under hyped aspect of quantum computing. What is

906
00:48:07,200 --> 00:48:09,719
Speaker 1: the thing that you're most excited about that would make

907
00:48:09,760 --> 00:48:12,160
Speaker 1: you like, take your family money and invest it in

908
00:48:12,239 --> 00:48:14,799
Speaker 1: somebody's quantum startup if you heard them doing it, if

909
00:48:14,840 --> 00:48:15,200
Speaker 1: I had that.

910
00:48:15,320 --> 00:48:16,480
Speaker 5: Kind of risk tolerance.

911
00:48:16,520 --> 00:48:18,600
Speaker 4: There are so many things that I knew about when

912
00:48:18,640 --> 00:48:20,800
Speaker 4: they were small, that I should have been investing in.

913
00:48:20,960 --> 00:48:23,759
Speaker 4: And you know, clearly it's probably for the best that

914
00:48:23,880 --> 00:48:27,520
Speaker 4: I became a professor and not an investor. I mean,

915
00:48:27,600 --> 00:48:29,520
Speaker 4: one thing I think that a lot of people don't

916
00:48:29,560 --> 00:48:32,239
Speaker 4: realize is that, you know, we are just within the

917
00:48:32,320 --> 00:48:36,719
Speaker 4: last year the experimentalists have gotten very very close to

918
00:48:37,480 --> 00:48:41,160
Speaker 4: the degree of control, you know, over cubits that would

919
00:48:41,200 --> 00:48:44,960
Speaker 4: be needed to build a scalable quantum computer with with

920
00:48:45,080 --> 00:48:49,160
Speaker 4: what we call quantum error correction, which is the technology

921
00:48:49,239 --> 00:48:51,680
Speaker 4: that would ultimately allow you to sort of keep your

922
00:48:51,800 --> 00:48:57,200
Speaker 4: cubits isolated, keep them from being prematurely measured by their environment,

923
00:48:57,640 --> 00:49:00,480
Speaker 4: and you know, do an arbitrarily long quantum come computation

924
00:49:00,640 --> 00:49:02,920
Speaker 4: with them. Right, So people have been talking about these

925
00:49:03,000 --> 00:49:05,920
Speaker 4: ideas for thirty years now, and you know, so some

926
00:49:06,080 --> 00:49:09,600
Speaker 4: people you may have gotten fatigue with quantum computing. What

927
00:49:09,719 --> 00:49:13,600
Speaker 4: we've known since the mid nineteen nineties is that if

928
00:49:13,680 --> 00:49:18,080
Speaker 4: you can control two pairs of cubits well enough, like

929
00:49:18,200 --> 00:49:21,239
Speaker 4: if you can get the error the noise in a

930
00:49:21,400 --> 00:49:25,080
Speaker 4: two cubit interaction to be sufficiently small, then there are

931
00:49:25,160 --> 00:49:28,680
Speaker 4: these very very clever quantum error correcting codes that can

932
00:49:28,760 --> 00:49:31,280
Speaker 4: get you the rest of the way that can encode

933
00:49:31,640 --> 00:49:35,920
Speaker 4: like a single logical cubit across an entangled state of

934
00:49:36,160 --> 00:49:39,239
Speaker 4: tens or hundreds of physical cubits, you know, in such

935
00:49:39,280 --> 00:49:42,160
Speaker 4: a way that you can survive and recover from, you know,

936
00:49:42,280 --> 00:49:43,640
Speaker 4: an error on any one of.

937
00:49:43,640 --> 00:49:44,719
Speaker 5: The physical cubits.

938
00:49:45,040 --> 00:49:48,800
Speaker 4: And you know, these codes have the effect of pushing

939
00:49:48,920 --> 00:49:53,319
Speaker 4: your effective error rate down closer and closer to zero. Right,

940
00:49:53,600 --> 00:49:57,000
Speaker 4: but only after you've passed this critical point at which

941
00:49:57,160 --> 00:50:00,840
Speaker 4: the error correction becomes a net win, which it starts

942
00:50:00,920 --> 00:50:04,600
Speaker 4: making things better rather than making them worse. It's almost

943
00:50:04,600 --> 00:50:06,960
Speaker 4: as if like you have to pass the critical mass

944
00:50:07,239 --> 00:50:10,200
Speaker 4: for a nuclear reaction. Right, if you're halfway there, you

945
00:50:10,239 --> 00:50:13,000
Speaker 4: don't get half the reaction, right, you know, you need

946
00:50:13,120 --> 00:50:16,080
Speaker 4: to pass criticality, okay, And so that's sort of been

947
00:50:16,160 --> 00:50:19,480
Speaker 4: the engineering goal of the people who, unlike me, have

948
00:50:19,719 --> 00:50:23,239
Speaker 4: labs and not just blackboards, right, who are actually trying

949
00:50:23,320 --> 00:50:25,759
Speaker 4: to build these devices. You know, that's been their goal

950
00:50:25,880 --> 00:50:28,439
Speaker 4: for thirty years, and I think you know, many people

951
00:50:28,600 --> 00:50:31,960
Speaker 4: might not realize just how close they are. So basically,

952
00:50:32,080 --> 00:50:34,000
Speaker 4: you know, the estimate is that you want to be

953
00:50:34,040 --> 00:50:37,920
Speaker 4: able to, you know, apply a two cubit gait, that is,

954
00:50:38,000 --> 00:50:41,400
Speaker 4: you know, do a desired operation on two cubits with

955
00:50:41,560 --> 00:50:45,839
Speaker 4: about ninety nine point nine nine percent accuracy, about four

956
00:50:45,960 --> 00:50:48,960
Speaker 4: nines of accuracy, and that ought to be enough to

957
00:50:49,120 --> 00:50:53,680
Speaker 4: get this quantum error correction, you know, self sustaining reaction started.

958
00:50:54,120 --> 00:50:57,000
Speaker 4: And when I joined the field, which was in the

959
00:50:57,120 --> 00:51:01,120
Speaker 4: late nineteen nineties, such as a few years after shores

960
00:51:01,160 --> 00:51:04,279
Speaker 4: and grovers algorithms had been discovered, right, it would have

961
00:51:04,360 --> 00:51:07,080
Speaker 4: been amazing if you could do like a two cubit

962
00:51:07,200 --> 00:51:10,920
Speaker 4: gate with fifty percent accuracy, right, Like that would have

963
00:51:10,960 --> 00:51:12,959
Speaker 4: been a nature paper, okay. But then at some point

964
00:51:13,040 --> 00:51:17,040
Speaker 4: the fifty percent became ninety percent, and then that became

965
00:51:17,200 --> 00:51:20,839
Speaker 4: ninety five ninety nine percent, and within the last year

966
00:51:21,000 --> 00:51:24,840
Speaker 4: we've seen like ninety nine point nine percent accuracy, okay,

967
00:51:25,040 --> 00:51:29,040
Speaker 4: in several different groups. You know, the neutral atoms grew

968
00:51:29,200 --> 00:51:34,279
Speaker 4: Querra and Boston, the trapped ions which like Quantinuum in

969
00:51:34,400 --> 00:51:39,279
Speaker 4: Colorado does superconducting cubits, which at Google and IBM are doing.

970
00:51:39,840 --> 00:51:42,120
Speaker 4: So you know, so a bunch of different approaches are

971
00:51:42,280 --> 00:51:45,560
Speaker 4: you know, being pursued simultaneously, but you know, several of

972
00:51:45,680 --> 00:51:48,160
Speaker 4: them are getting up to this, like ninety nine point

973
00:51:48,239 --> 00:51:51,440
Speaker 4: nine percent accuracy, and it looks like just one more

974
00:51:51,560 --> 00:51:53,920
Speaker 4: nine and you should be at the point where you

975
00:51:53,960 --> 00:51:57,120
Speaker 4: know this self sustaining reaction works. So I think that's

976
00:51:57,200 --> 00:52:00,960
Speaker 4: the central case for optimism right now. For just looking

977
00:52:01,040 --> 00:52:03,040
Speaker 4: at the hour raid as a function of year, it

978
00:52:03,120 --> 00:52:05,440
Speaker 4: looks like either you get there in the next decade

979
00:52:05,760 --> 00:52:07,600
Speaker 4: or else something surprising.

980
00:52:07,160 --> 00:52:10,080
Speaker 5: Happens, you know that explains why you didn't get there.

981
00:52:10,640 --> 00:52:13,320
Speaker 4: That's one aspect of the story that maybe is not

982
00:52:13,520 --> 00:52:14,600
Speaker 4: so well appreciated.

983
00:52:15,600 --> 00:52:17,719
Speaker 1: All right, So that was our interview with Scott in

984
00:52:17,760 --> 00:52:21,640
Speaker 1: conversation about quantum computing. I think the answer to the

985
00:52:21,760 --> 00:52:25,239
Speaker 1: question should you be excited about quantum computing is yes.

986
00:52:25,760 --> 00:52:30,560
Speaker 1: This potentially represents a whole new era of computing computers

987
00:52:30,760 --> 00:52:33,520
Speaker 1: that are good at solving different kinds of problems than

988
00:52:33,560 --> 00:52:37,160
Speaker 1: our traditional computers and opens our minds to the way

989
00:52:37,320 --> 00:52:40,560
Speaker 1: computation can be done. Maybe there are other kinds of computers,

990
00:52:40,640 --> 00:52:44,080
Speaker 1: not just classical or quantum computers, but other kind of things.

991
00:52:44,400 --> 00:52:46,840
Speaker 1: Ways we can take advantage of the universe to do

992
00:52:46,960 --> 00:52:50,200
Speaker 1: the calculations that we want to do. Thanks Scott very

993
00:52:50,280 --> 00:52:52,640
Speaker 1: much for joining us, and thank you to everybody for listening.

994
00:52:52,800 --> 00:52:53,640
Speaker 1: Tune in next time.

995
00:53:00,719 --> 00:53:04,520
Speaker 3: Daniel and Kelly's Extraordinary Universe is produced by iHeartRadio. We

996
00:53:04,600 --> 00:53:06,960
Speaker 3: would love to hear from you, We really would.

997
00:53:07,200 --> 00:53:09,840
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998
00:53:10,160 --> 00:53:11,760
Speaker 1: Extraordinary Universe.

999
00:53:11,920 --> 00:53:14,839
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1000
00:53:14,880 --> 00:53:17,879
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1001
00:53:17,920 --> 00:53:18,279
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1002
00:53:18,560 --> 00:53:22,000
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1003
00:53:22,120 --> 00:53:24,720
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1004
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1005
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1006
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1007
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