Speaker 1: Hey, everyone, Daniel here. You know you hear the word
Speaker 1: quantum a lot, like a lot a lot, mostly in
Speaker 1: places where it makes no sense. I see it on
Speaker 1: advertisements for pest control companies or financial firms or laser
Speaker 1: tattoo joints. Okay, lasers actually are kind of quantum, so
Speaker 1: maybe that one works. But the point is that the
Speaker 1: word is so ubiquitous that it's come to mean very little.
Speaker 1: But it does have meaning. It evokes something modern and mysterious,
Speaker 1: because in the last one hundred years, physics has discovered
Speaker 1: that the world is very mysterious and operates under very
Speaker 1: different rules than the ones we are used to the
Speaker 1: world that we experience, the one that Aristotle and Copernicus
Speaker 1: and Galleo and Newton and even Einstein we're trying to
Speaker 1: understand is something of an illusion. When we peel back
Speaker 1: that layer of reality and see what's happening underneath, we
Speaker 1: discover that the rules of the microscopic universe are very different,
Speaker 1: almost alien to our intuition. And so of course people wondered,
Speaker 1: almost immediately, hey, what can I do with this? Can
Speaker 1: I take advantage of that to make my video games
Speaker 1: faster or hack into my school computer and change my
Speaker 1: grades because hey, what else is fundamental physics?
Speaker 2: Good form? All right?
Speaker 1: But jokes aside, quantum computing is something you hear a
Speaker 1: lot about these days. There's a lot of information and
Speaker 1: plenty of misinformation out there. Is it just another marketing
Speaker 1: ploy like quantum wasps appers or is it like the
Speaker 1: first computing revolution which completely transformed our society, our economy,
Speaker 1: and our daily lives. So today on the podcast, we're
Speaker 1: excited to be talking to Professor Scott Arenson, one of
Speaker 1: the world's top experts in quantum computing and a renowned
Speaker 1: communicator who can help us answer the question should we
Speaker 1: be excited about quantum computing? Welcome to Daniel and Kelly's
Speaker 1: Extraordinary Universe.
Speaker 3: Hello, I'm Kelly Waiter Smith, and I sort of think
Speaker 3: I understand how quantum computing works, but I'm never really sure.
Speaker 4: Hi.
Speaker 1: I'm Daniel. I'm a particle physicist, and I simultaneously understand
Speaker 1: and don't understand quantum computing.
Speaker 3: That's very appropriate. I think I think I understand it
Speaker 3: enough to get that joke, which makes me feel pretty good.
Speaker 1: Oh, you're pretty clever.
Speaker 3: So what is your favorite product that has been pitched
Speaker 3: as being quantum that makes you laugh.
Speaker 1: I once sat next to somebody on airplane who told
Speaker 1: me she was a quantum messuse. I was really wondering
Speaker 1: about how that worked, and I asked her a bunch
Speaker 1: of questions and I didn't reveal my expertise, but I
Speaker 1: didn't learn anything about quantum mechanics, and that conversation just
Speaker 1: something about.
Speaker 5: People, you know, yeah, yep.
Speaker 3: It was it like maybe she's giving you a massage,
Speaker 3: maybe she's not, and you don't know until you pay.
Speaker 3: That's the thing that reveals if it happened or not.
Speaker 1: No, it was more like spooky action at a distance,
Speaker 1: because she massages you without actually touching you. She like
Speaker 1: waves her hands nearby, and that somehow, through quantum mechanics
Speaker 1: influences your muscles and joints and whatever. So she was
Speaker 1: charging a lot of money. Also, people were paying her
Speaker 1: big bucks for this effect.
Speaker 3: I would be so grumpy if I paid for a
Speaker 3: massage like that, like you really got to get into
Speaker 3: my muscles if I'm paying you. But anyway, okay, well
Speaker 3: that's amazing. I once heard it was a quantum self
Speaker 3: help talk, and the whole time I just couldn't, could
Speaker 3: not understand how quantum, like how thinking about it from
Speaker 3: a quantum way was helpful at all, And it just
Speaker 3: left me completely baffled.
Speaker 1: I think people just latching onto the idea that the
Speaker 1: world is fundamentally different from the world we thought it was,
Speaker 1: that it works in a different way, it follows different rules,
Speaker 1: and that feels a little bit like magic. You can
Speaker 1: sort of grab onto that and smear your business with
Speaker 1: a little bit of that sparkly quantum physics stuff, And
Speaker 1: this feels like maybe you can do something which seemed impossible.
Speaker 3: When I think quantum is particularly likely to get used
Speaker 3: in that way because it's confusing. I mean, maybe it's
Speaker 3: not confusing to the people who study it, but for
Speaker 3: like lay people, it just sounds so unclear. And I
Speaker 3: think that you can feel like you understand it and yeah,
Speaker 3: but definitely feel like using the word quantum makes you
Speaker 3: sound smart and then you just run with it. And
Speaker 3: so I'm excited to see what our audience thinks quantum
Speaker 3: computing is all about, because I until I married Zach,
Speaker 3: who likes giving me literally three hour lectures on car
Speaker 3: rides about what quantum computing is. Well, I'm driving and
Speaker 3: can't escape. I didn't feel like I knew. So let's
Speaker 3: see what does the you know, what does the general public,
Speaker 3: in particular our audience think quantum computing is.
Speaker 1: So I reached out to our listeners and I asked
Speaker 1: them what's different about it quantum computer. Here's what listeners
Speaker 1: had to say.
Speaker 6: Quantum computers do multiple calculations at once, and each time
Speaker 6: you add a bit it goes up by exponential numbers faster.
Speaker 7: But especially they do not overheat.
Speaker 1: I don't know anything about quantum computers.
Speaker 8: You can actually get an answer faster because you can
Speaker 8: determine the probabilities rather than brute forcing it.
Speaker 9: Quantum computer works with superpositions instead of ones and zeros.
Speaker 9: That gives it advantages for some calculations, but makes it
Speaker 9: worse for other calculations.
Speaker 8: But quantum computer is like having five hundred and eighty
Speaker 8: thousand people all typing one word of the book, all
Speaker 8: at the same time, but in order.
Speaker 4: A quantum computer is different because you'll never show with
Speaker 4: it to turn it on and off again or off
Speaker 4: and on again.
Speaker 1: A quantum particle can be in.
Speaker 2: A superposition on off or on and off at the
Speaker 2: same time.
Speaker 3: A quantum computer can use three bits I think spin up,
Speaker 3: spin down, and I think something in between.
Speaker 10: A quantum computer runs on quantum mechanics, which makes it
Speaker 10: capable of calculating the probabilities in a situation where randomness
Speaker 10: is involved.
Speaker 8: Does a quantum computer not use binary code zeros and
Speaker 8: ones so there can be more options, which.
Speaker 9: Makes it more powerful.
Speaker 2: Possibly quantum computers use q bits that are super cooled
Speaker 2: and have many many states rather than the binary transistor
Speaker 2: logic that only has off and on.
Speaker 5: Quantum computing is all about probabilities.
Speaker 4: With a quantum computer, you can't tell if it's on
Speaker 4: or off until you hope the box it came in.
Speaker 11: A quantum computer uses quantum fluctuation for its processor and
Speaker 11: explores every possible answer instantaneously.
Speaker 4: How this is useful for our computation is still beyond
Speaker 4: my understanding.
Speaker 11: Computer uses quantum technology to search all the random variables
Speaker 11: that possibly happen and come up a handurd solution.
Speaker 12: I think quantum computers process really small numbers. Do they
Speaker 12: do computations on extremely tiny numbers? I'm not sure what
Speaker 12: is different about a quantum computer that.
Speaker 10: Makes use of superposition of states to give nearly infinite possibilities.
Speaker 11: The size of the processor or the flip flops. So
Speaker 11: now we're down on a quantum level versus the smallest
Speaker 11: transistors we have at this moment.
Speaker 5: Other them its fast are really about nothing.
Speaker 7: What's different about a quantum computer. I don't really know
Speaker 7: much about how quantum computers work, but there's something fundamentally
Speaker 7: different about how the bits, essentially we're being.
Speaker 8: On or off.
Speaker 7: And that's not something I know the details of.
Speaker 3: I'm not surprised that our audience had detailed and insightful
Speaker 3: responses to this question.
Speaker 4: That is awesome, But you know.
Speaker 3: You and I decided this is a complicated question, and well,
Speaker 3: I'm sure Daniel could have explained it on his own.
Speaker 3: We thought, you know, it might be a good idea
Speaker 3: to bring in Scott Aronson, who's an expert on quantum computing,
Speaker 3: has been working on it for a really long time,
Speaker 3: and so we're bringing you a conversation that's a collaboration
Speaker 3: between Daniel and Scott Aronson. So, Daniel, where do we start?
Speaker 1: So I think the place to start with understanding quantum
Speaker 1: computers is first with the word computer, Like what do
Speaker 1: we mean when we say computer? And this isn't just
Speaker 1: like philosophical rabbit hole. I think it's important to think
Speaker 1: about what a can is, what a computer does, not
Speaker 1: just the kinds of computers that we have, but other
Speaker 1: kinds of computers, what possible computers there are, because then
Speaker 1: we can compare the different kinds of computers, we can
Speaker 1: understand what they have in common and what they don't
Speaker 1: have in common. Because computers are not just something that
Speaker 1: sits on your desk or the thing inside your phone
Speaker 1: that makes it go. A computer basically is something that computes.
Speaker 1: It's something that gives you an answer to a question,
Speaker 1: you know, and that can be something very simple, like
Speaker 1: you can have a baseball and you throw the baseball.
Speaker 1: That gives you the answer to what happens when you
Speaker 1: throw a baseball, and that can be kind of a
Speaker 1: complicated calculation. You know, you have to take into account
Speaker 1: gravity and air resistance and the spin of the Earth
Speaker 1: and all this kind of stuff, and the baseball does
Speaker 1: that for you. It computes the answer to that question.
Speaker 1: And that's not a very exciting computer because that's basically
Speaker 1: all it can do other than let you play baseball.
Speaker 1: That one just answers one question. In general, we're interested
Speaker 1: in computers that can do lots of different kinds of calculations,
Speaker 1: and one kind of computer is you. You know, back
Speaker 1: in the middle of last century, what they called a
Speaker 1: computer was a person like at NASA who sat at
Speaker 1: a table doing calculations pencil and paper to figure out
Speaker 1: how are we going to shoot this rocket and what
Speaker 1: angle do we need to go at, because that was
Speaker 1: the best way to do those calculations. And humans pretty
Speaker 1: good at doing lots of different calculations. So a computer
Speaker 1: can include the thing on your desk, the thing in
Speaker 1: your phone, a baseball, even a person, right, and all
Speaker 1: these computers, some of them are good at different kinds
Speaker 1: of calculations, Like the ball is excellent at calculating where
Speaker 1: the ball is going to go, but terrible at everything else.
Speaker 1: A human is pretty good at some kinds of things
Speaker 1: and not that great at other kinds of things. Like
Speaker 1: a human is really good at doing things like spotting
Speaker 1: a tiger in the grass. Your brain is really efficient
Speaker 1: at noticing things that look like predators, not so great
Speaker 1: at other things like adding up all the numbers between
Speaker 1: one and a trillion. I mean, maybe you can find
Speaker 1: some mathematical shortcut, but if you have to actually add
Speaker 1: them up, it would take you a long time. It's
Speaker 1: not the kind of thing your brain is efficient at now.
Speaker 1: The kind of computer where, of course are interested in.
Speaker 1: It's not baseballs or tigers. It's the kind that are programmable,
Speaker 1: the kind that can basically do any kind of calculation.
Speaker 1: And this is something that's kind of incredible that we
Speaker 1: can even do. We have a question we want answer,
Speaker 1: it's some calculation we want done, and we build a
Speaker 1: computer which is like a machine, build a physical objects
Speaker 1: that we can manipulate in a way to do our calculation.
Speaker 1: That's the amazing thing about programmable computers that when you
Speaker 1: build it, you don't have to know what kind of
Speaker 1: calculations you want it to do. You can build it
Speaker 1: in such a way that it can do almost any
Speaker 1: kind of calculation. That's sort of amazing. We're taking a
Speaker 1: real world problem and then we're building a device that
Speaker 1: can calculate the answer as long as we can represent
Speaker 1: that real world problem somehow in the language of that computer.
Speaker 1: So these are deep issues and fascinating questions. To help
Speaker 1: us understand what is computing and how quantum computing is
Speaker 1: different from regular normal computing, we talk to Professor Scott Arenson.
Speaker 1: He he's a professor of theoretical computer science at the
Speaker 1: University of Texas at Austin. He's worked with open AI
Speaker 1: on the theoretical foundations of AI safety. He writes a
Speaker 1: wonderful blog about quantum computing called stettl Optimized, and he's
Speaker 1: maybe most famously the author of quantum computing since democratis.
Speaker 1: And he's a lot of fun to talk to. So
Speaker 1: we're very glad to have Scott on the show. And
Speaker 1: our first question for Scott, who's a professor of theoretical
Speaker 1: computer science, is what is theoretical computer science anyway?
Speaker 4: So it's a field that's usually considered to have started
Speaker 4: with Alan Touring in the nineteen thirties, who came up
Speaker 4: with a theoretical model of what a computer could be,
Speaker 4: which was called the touring machine.
Speaker 1: So when Scott is talking about a touring machine, he's
Speaker 1: not talking about any kind of computer anybody's ever actually built.
Speaker 1: This is like the computer science equivalent of a thought experiment, right,
Speaker 1: It's a thought computer. It's something Alan Turing came up with.
Speaker 1: It's the simplest kind of computer he could imagine, and
Speaker 1: it's a computer that, again you would never build. But
Speaker 1: it's very simple. Imagine a computer that has a tape
Speaker 1: on which there are written numbers zeros and ones, for example,
Speaker 1: and you can read the tape. It can move the
Speaker 1: tape forwards and backwards. They can also write to the tape,
Speaker 1: so you can do all the basic stuff that we
Speaker 1: think computers can do. Right. It can read in information,
Speaker 1: it can do a calculation and decide what it's going
Speaker 1: to do next. It can write onto the tape, so
Speaker 1: it can modify that. It can store information. And Turing
Speaker 1: did something really cool, which is that he showed that
Speaker 1: a Turing machine is very simple, basic kind of thought.
Speaker 1: Computer can do any kind of calculation that any computer
Speaker 1: could do. So if it was doable on a computer,
Speaker 1: you could do it on a Turing machine. That doesn't
Speaker 1: mean a Turing machine is the best way to do
Speaker 1: it or the right way to do it, but because
Speaker 1: the Turning machine is so simple, it let you think
Speaker 1: about the kind of things computers could do.
Speaker 2: Well.
Speaker 1: You could say, if you can do it on a
Speaker 1: Turing computer, then you could do it on any computer.
Speaker 1: And it's easier to prove theorems and think about stuff
Speaker 1: on a Touring computer. And again, a Turing machine doesn't
Speaker 1: lend me you to thinking about the kinds of computer
Speaker 1: that we've been building. It can explore the kind of
Speaker 1: things any computer can do.
Speaker 4: When Touring used the word computer in the nineteen thirties,
Speaker 4: you know, that was what it meant people, usually women,
Speaker 4: who were hired to do computation. And he came up
Speaker 4: with his model of the Touring machine by trying to
Speaker 4: idealize what such a person would be doing. You know
Speaker 4: that they'd be reading symbols on a piece of paper,
Speaker 4: maybe you know, crossing them off, replacing them with new symbols,
Speaker 4: you know, moving back and forth on the paper. They
Speaker 4: would always be doing so according to some rule that
Speaker 4: they knew or had been taught. There is nothing in
Speaker 4: the concept that is tied to you know, it has
Speaker 4: to be built out of transistors that are etched onto
Speaker 4: wafers of silicon.
Speaker 2: Right.
Speaker 4: That happens to be the way that we do it
Speaker 4: now because it worked so spectacularly. Well, okay, but you
Speaker 4: know a computer could in principle be made out of
Speaker 4: billiard balls, right, It could be made out of jets
Speaker 4: of water.
Speaker 2: Right.
Speaker 5: You know, these would maybe not be very reliable.
Speaker 1: Methods, and also doesn't have to be programmable and infinitely powerful. Right,
Speaker 1: anytime I build an experiment, I'm building a computer.
Speaker 4: Until very recently, you know, there was use in analog computers, right,
Speaker 4: and people just building analog systems to simulate some physical process.
Speaker 4: You know, at some point digital computing became so good
Speaker 4: that it eliminated the use cases for that.
Speaker 1: So it's great that the kind of digital computers we've
Speaker 1: been building can basically solve any problem. I mean, there
Speaker 1: are cobbyists to that, there are things turn computers can't do.
Speaker 1: But the crucial thing for understanding quantum computing and computing
Speaker 1: more generally is that some problems are hard, and some
Speaker 1: problems are easy. Some problems you can do quickly, and
Speaker 1: some problems you can do very very slowly. And different
Speaker 1: kind of computers are going to be good at different
Speaker 1: kinds of things, so the same with it. Like you
Speaker 1: as a person, you are a computer. You are fast
Speaker 1: or slow at particular problems. Our style of computers, what
Speaker 1: we call classical digital computers. These are good at some
Speaker 1: kinds of things and slow at other kinds of things.
Speaker 1: It's a feature of the kind of computers we've long developed, which,
Speaker 1: of course you know isn't the only way to do it.
Speaker 1: That's where we're going. But the origins of the digital
Speaker 1: computer that we've been using go all the way back
Speaker 1: to the middle of last century with John von Neuman.
Speaker 4: John von Neuman, who built some of the first digital computers,
Speaker 4: was you know, directly inspired by tourings insights, in particular
Speaker 4: the idea that you didn't want to build a different
Speaker 4: machine for each possible function. You wanted to build one
Speaker 4: universal machine and then have it simulate any other machine by.
Speaker 5: Giving it an appropriate program.
Speaker 4: This is, you know, an idea that today is so
Speaker 4: obvious that, you know, it's hard to convince my students
Speaker 4: that it was ever non obvious. So today what we
Speaker 4: do in theoretical computer science is often about trying to
Speaker 4: understand what problems can be solved efficiently.
Speaker 1: All right, And so when Scott talks about computers being efficient,
Speaker 1: he's thinking about whether they're good at this kind of problem?
Speaker 1: Can they do it quickly? As a problem gets bigger
Speaker 1: and bigger, do they become unbearably slow at solving it?
Speaker 1: Like you can add up the numbers between one and
Speaker 1: five pretty quickly, you can add up the number between
Speaker 1: one and one hundred, little less quickly. If it gets
Speaker 1: to one in a trillion, it's hopeless, right, And so
Speaker 1: for digital computers there are some problems that can be
Speaker 1: solved in principle but would take a very very long time,
Speaker 1: and the kind of computer we've been building. An example
Speaker 1: of that problem is checking to see if a number
Speaker 1: is prime. Like you can tell that eleven is prime
Speaker 1: because you can't think of any two numbers that multiply
Speaker 1: themselves together to give you eleven because it is prime.
Speaker 1: If I give you an arbitrary number seven, four hundred
Speaker 1: and seventeen, how do you know whether it's prime. Well,
Speaker 1: a computer can do this by, for example, checking all
Speaker 1: the numbers that go into it. That's just brute forcing it.
Speaker 1: There are some more clever algorithms we'll hear about later,
Speaker 1: but essentially it's very slow at checking prime numbers. So
Speaker 1: computers can do a lot of things, and the way
Speaker 1: we've usually built computers are good at some things and
Speaker 1: slow at other things. So we ask Scott, who thinks
Speaker 1: about this a lot, what kind of things our normal
Speaker 1: computers are good at and our normal computers are bad at?
Speaker 2: Sure?
Speaker 4: So simulating physics, you know, which you mentioned, is a
Speaker 4: great example of you know, something that computers have been
Speaker 4: used for since the very beginning, right and in some sense,
Speaker 4: you know, the entire program of physics since Galileo and
Speaker 4: Newton has been to you know, understand nature. Well. It
Speaker 4: off that we can put the initial conditions into a
Speaker 4: computer and just have a computer tell us what is
Speaker 4: going to happen.
Speaker 5: Simulating a differential equation, you know.
Speaker 4: In order to model fluids or gravitational dynamics, celestial mechanics.
Speaker 4: These are our famous examples of things that you know,
Speaker 4: comput uters.
Speaker 5: Are very good at. There are issues here, you know,
Speaker 5: that have to do with discritization.
Speaker 4: Nature is traditionally modeled in physics as using a continuum
Speaker 4: of numbers, and computers in the touring sense can only
Speaker 4: deal with discrete quantities with bits with ones and zeros.
Speaker 4: So we need some way to deal with that, right,
Speaker 4: Typically we take continuous quantities and we truncate them, we
Speaker 4: represent them only to a finite precision, and then you know,
Speaker 4: we have to understand how.
Speaker 5: Much error that's going to cause and so forth.
Speaker 4: But you know, in terms of what computers can do efficiently,
Speaker 4: you know, I think the classic examples that you know,
Speaker 4: we would teach in computer science are going to be
Speaker 4: things like, Okay, you know all of the arithmetic operations
Speaker 4: that we learn in school, right, adding to integers, you know, multiplying,
Speaker 4: dividing given Also, you know the thing that Google Maps
Speaker 4: does for us, right, find the shortest route between two
Speaker 4: given cities, you know, or between two addresses in the
Speaker 4: distance between each address and the immediately neighboring ones.
Speaker 5: Right, it's finding the shortest path in a graph.
Speaker 4: Okay. Now, there are a bunch of things that turn
Speaker 4: out to have clever efficient algorithms, even though it is
Speaker 4: sort of totally not obvious a priori that they would.
Speaker 4: For example, I give you a bunch of students, you know,
Speaker 4: I tell you who is willing to be roommates with whom? Okay,
Speaker 4: and now I ask you pair off as many willing
Speaker 4: roommates as you can. Right. This is called the maximum
Speaker 4: matching problem. Find you know, maximum number of pairs of
Speaker 4: people who are willing to room with each other A priori.
Speaker 4: That seems like that might require an exponential search, right,
Speaker 4: It might require considering an astronomical number of possibilities.
Speaker 5: But it was discovered in the nineteen sixties that it doesn't.
Speaker 4: There is an efficient way to solve that, That is,
Speaker 4: there's a way to solve it that scales only like
Speaker 4: the cube of the number of potential roommates something like that,
Speaker 4: rather than exponentially.
Speaker 5: Another great example would be linear.
Speaker 4: Programming, right, one of the most important problems in industrial
Speaker 4: you know, operations research, things like that, where you're given
Speaker 4: a bunch of linear constraints on some variables like this
Speaker 4: one plus this one can be at most ten, this
Speaker 4: one minus this one has to be at least eight,
Speaker 4: and so forth, and you're looking for a solution that
Speaker 4: satisfies all of those linear constraints. Okay, that also has
Speaker 4: an efficient solution primality. Right, I give you five thousand
Speaker 4: digit number, and I ask you is it prime or composite?
Speaker 2: Right?
Speaker 5: Well, you know you can try some simple things.
Speaker 4: You know, if it ends in an even number, or
Speaker 4: a zero or a five, right, then it's composite. But
Speaker 4: you know you can check if three, if seven, if eleven,
Speaker 4: go into it, right, But more generally, right, this is
Speaker 4: a famous problem in math. It's even extremely important in
Speaker 4: cryptoc Modern cryptography sort of uses gigantic prime numbers as
Speaker 4: one of its central ingredients.
Speaker 5: Okay, Now it turns out that there is.
Speaker 4: An efficient algorithm that tells you whether a huge number
Speaker 4: is prime or composite.
Speaker 5: Okay, it was discovered in the nineteen seventies.
Speaker 4: There are probabilistic methods, and in two thousand and two
Speaker 4: even a deterministic method was discovered.
Speaker 5: Okay, you're very very non obvious.
Speaker 4: Now, Crucially, these methods only tell you if the number
Speaker 4: is prime or composite. If it's composite, they don't tell
Speaker 4: you what the prime factors.
Speaker 1: Are, all right. So those are some great examples of
Speaker 1: what classical computers are pretty good at. What kind of
Speaker 1: things are they slow at? Again, a great example is
Speaker 1: prime factors. And this is really important because it turns
Speaker 1: out that it's useful to a lot of people that
Speaker 1: computers are slow at this. Like, if you know that
Speaker 1: computers can't crack this puzzle quickly, you can use it
Speaker 1: as a way to protect your information. The whole field
Speaker 1: of cryptography, of building codes and protecting information relies on
Speaker 1: some things being easy and some things being hard for
Speaker 1: computers to do.
Speaker 4: The belief that finding the prime factors is hard is
Speaker 4: actually also central to modern cryptography. So modern cryptography, you
Speaker 4: have to be able to generate huge prime numbers quickly
Speaker 4: multiply them together quickly, which we know how to do
Speaker 4: all of that. That's how we generate the cryptographic keys
Speaker 4: called the public keys, that people can use to send
Speaker 4: us encrypted messages. But then the way that it works
Speaker 4: is that in order to decrypt the message, we.
Speaker 5: Think, you need to know the prime factors.
Speaker 4: If anyone had a fast way to find the prime
Speaker 4: factors of a gigantic composite number, most of the cryptography
Speaker 4: that protects the Internet would be broken.
Speaker 5: Okay, so it is crucial that.
Speaker 4: You know, after half a century of effort, at least
Speaker 4: the best publicly known methods for factoring numbers.
Speaker 8: You know.
Speaker 4: Of course, if the NSA you know knew something better,
Speaker 4: then you know, I would have no reason to know that.
Speaker 4: But the best publicly known methods you know use an
Speaker 4: amount of time that scales exponentially with the number of digits,
Speaker 4: or more precisely, exponentially with the cube root of the
Speaker 4: number of digits.
Speaker 1: So when Scott talks about scaling with the number of digits,
Speaker 1: what he's talking about is how long it will take
Speaker 1: the computer to solve the puzzle, depending on the length
Speaker 1: of the password. Problems that get much harder very quickly
Speaker 1: when you add digits to your password. Those are good
Speaker 1: for cryptography because it makes it easy to make the
Speaker 1: problem impossible even if computers get faster. Like if computers
Speaker 1: suddenly tomorrow get twice as faster next year they're five
Speaker 1: times as fast. You don't want people to be able
Speaker 1: to crack your passwords. The good thing about the prime
Speaker 1: number puzzle is you can just add a couple more
Speaker 1: digits to our keys to our passwords, and now the
Speaker 1: puzzle is impossible. Again, this problem is easy to make
Speaker 1: much harder because it gets exponentially harder as it gets bigger.
Speaker 1: So it's easy to keep making the problem harder faster
Speaker 1: than computers are getting better at the problem. That's the
Speaker 1: key to these exponential problems. And there are a bunch
Speaker 1: of problems like this that are very hard to solve
Speaker 1: for our classical digital computers.
Speaker 4: So factoring is a famous example of a problem that
Speaker 4: might be exponentially hard for all we know. Okay, but
Speaker 4: there's maybe an even more famous class of problems that
Speaker 4: are believed to be exponentially hard. And this includes, like
Speaker 4: most of the problems in combinatorial search and optimization that
Speaker 4: people care about in practice, and so examples would be,
Speaker 4: I give you the distances between every city and every other.
Speaker 4: I ask you to find the shortest path that visits
Speaker 4: every city. That's the famous traveling salesman problem traveling salesperson.
Speaker 5: Call it today.
Speaker 4: Or I give you the mess of a bunch of suitcases.
Speaker 4: I asked, can they all fit in the trunk of
Speaker 4: your car? That's a problem with which many of us
Speaker 4: have experience.
Speaker 1: The answer is always yes, you have to try.
Speaker 4: Arranging them in a different way. Or you know, I
Speaker 4: give you a jigsaw puzzle, you know, can you solve it?
Speaker 4: To make it hard, let's imagine a jigsaw puzzle with
Speaker 4: no picture on it, okay, or a sudoku.
Speaker 1: The answer is always you can solve. It's just a
Speaker 1: question of how many curse words are going to be the.
Speaker 4: Answer yes, yes, And if it's thousands of pieces, then
Speaker 4: are we talking about more curse words than there are
Speaker 4: atoms in the observable universe.
Speaker 1: So we've reminded ourselves what it digital computer can do.
Speaker 1: Some things it can do really well and very quickly,
Speaker 1: and other things it does more slowly, and as a
Speaker 1: problem to bigger, becomes unbearably slow. But the topic of
Speaker 1: today's episode, of course, is other kinds of computers. What
Speaker 1: if you decided to build a computer using something other
Speaker 1: than zeros and ones. Remember, computer is just some arrangement
Speaker 1: of a physical system that lets you do a calculation.
Speaker 1: Doesn't have to be based on like bits that flip
Speaker 1: between zero and one and half precise values. What if
Speaker 1: you found something in the universe that operated differently, that
Speaker 1: wasn't so crisp, right, that operated under a different set
Speaker 1: of rules that you could then exploit to do different
Speaker 1: kinds of calculations, or to have some calculations be faster
Speaker 1: or slower. That would be awesome because it would complement
Speaker 1: our current computer system.
Speaker 2: Right.
Speaker 1: We already know that this is possible in principle. You
Speaker 1: know my example of a baseball that can do a
Speaker 1: very precise calculation that includes all sorts of effect wind
Speaker 1: resistance and the tug of Jupiter and all sorts of
Speaker 1: stuff that would be very laborious for a traditional computer
Speaker 1: to do. It can do it very very quickly, in
Speaker 1: like the time you throw a baseball. But the question,
Speaker 1: and of course, is can you make a programmable computer
Speaker 1: not a specialized one off thing like a baseball, A
Speaker 1: programmable computer that is good at the kinds of problems
Speaker 1: that classical computers are slow at. And that's where quantum
Speaker 1: mechanics comes in, because quantum mechanics does operate under different rules,
Speaker 1: and we can exploit those rules to build a different
Speaker 1: kind of computer. The crucial things to understand about quantum
Speaker 1: mechanics in just a few minutes are that rather than
Speaker 1: having to have definitive states like this bit is zero
Speaker 1: or this bit is one, quantum bits from what we
Speaker 1: call cubits, can be in a superposition of states. A
Speaker 1: superposition just means it has a chance to be in
Speaker 1: more than one state, So rather than being a zero
Speaker 1: or a one, it can be like, well, this one
Speaker 1: has a thirty percent chance of being a zero and
Speaker 1: a seventy percent chance of being a one. That one
Speaker 1: over there has a ninety percent chance of being a
Speaker 1: zero and a ten percent chance of being a one.
Speaker 1: So a superposition just means two things laid on top
Speaker 1: of each other doesn't have to be zero or one.
Speaker 1: It can be some probability of zero or one. And
Speaker 1: the fact that it can maintain these superpositions lets it
Speaker 1: do something that classical bits can't do, which is interfere
Speaker 1: If you've heard of the double slit experiment, this is
Speaker 1: like a beam of photons that go through two slits,
Speaker 1: and maybe the photons come from one slit, and maybe
Speaker 1: the photons go through another slit, and the possibility that
Speaker 1: they've gone through both slits interferes with each other, creating
Speaker 1: this interference pattern on the final screen. And it's the
Speaker 1: fact that the photons can be in a superposition of
Speaker 1: those states, that they can be maybe through slit one
Speaker 1: and maybe through slit two. Those possibilities are the things
Speaker 1: doing the interfering. So that interference is very important part
Speaker 1: of what quantum systems can do and what classical systems
Speaker 1: cannot do because the classical system like if you throw
Speaker 1: a base ball through a double slit experiment, it either
Speaker 1: went through the left one or through the right one.
Speaker 1: There's no superposition and there's no interference. Now these are
Speaker 1: quantum systems that can do this weird thing of having
Speaker 1: multiple possibilities at the same time. But then when quantum
Speaker 1: systems interact with classical systems, when you ask the quantum system, hey,
Speaker 1: what is the value of this bit? Because eventually you
Speaker 1: want to know the answer from your computer, right you
Speaker 1: have to make a measurement, you have to do something,
Speaker 1: you interact with it. That's when the universe picks one
Speaker 1: of the options. So there's a spread of possible outcomes
Speaker 1: for any quantum interaction. There's a superposition that describes the
Speaker 1: various possibilities, and measurement is the thing that collapses it
Speaker 1: that makes the universe pick one of those outcomes. So
Speaker 1: this is the way the universe works in the microscopic scale,
Speaker 1: which is weird and amazing and very different from our
Speaker 1: experience and the macroscopic scale, and it opens the door
Speaker 1: to doing different kinds of computation. Remember a computer, it's
Speaker 1: just a physical system we've arranged to calculate something we're
Speaker 1: interested in. We take advantage of the way that physical
Speaker 1: system works to represent some kind of calculation and hope
Speaker 1: that that computer that we've built is good at that
Speaker 1: kind of calculation. And because quantum computers work with these
Speaker 1: very different rules, it means they can do different kinds
Speaker 1: of calculation, and they can do different calculations quickly, and
Speaker 1: they have different strengths and weaknesses than classical computers. So
Speaker 1: here's Scott telling us more about that.
Speaker 4: Let's now imagine a computer that operates by these principles
Speaker 4: of quantum mechanics, we'll call it a quantum computer, so.
Speaker 5: A classical computer.
Speaker 4: Typically, for simplicity, we like to imagine it as having
Speaker 4: a state that's built up out of bits out of
Speaker 4: you know, zeros are ones, right, and if there's one
Speaker 4: thousand bits, then there's two to one thousand power possible
Speaker 4: configurations of that computer's memory, okay. But now with a
Speaker 4: quantum computer, there's going to be vastly more configurations than that, okay,
Speaker 4: because if I have even one quantum bit or you know,
Speaker 4: what we call a cube bit, right, then it can
Speaker 4: have some amplitude for being zero and some amplitude for
Speaker 4: being one at the same time. So it can be
Speaker 4: in what we call a superposition of the zero state
Speaker 4: and the one state, you know, at.
Speaker 5: Least before we look at it.
Speaker 4: Once we make a measurement, then we'll force it to
Speaker 4: snap to either zero or one, and then it will
Speaker 4: probabilistically collapse to one or the other.
Speaker 5: But before we look it can be in this superposition state.
Speaker 5: And now if I have let's.
Speaker 4: Say three cubits, okay, then it's not enough to give
Speaker 4: amplitudes for each cubit separately from the others. Right, The
Speaker 4: rules of quantum mechanics are unequivocal. I have to give
Speaker 4: an amplitude that the three bits are zero zero zero.
Speaker 4: I have to give an amplitude that there are zero
Speaker 4: zero one. I have to give an amplitude that they're
Speaker 4: zero one zero, you know, and so on, so I
Speaker 4: have to give eight amplitudes.
Speaker 1: So it's sort of more information dense because the amount
Speaker 1: of information grows exponentially. But why does that allow us
Speaker 1: to do different kinds of computation? Why does that allow
Speaker 1: us to solve different kinds of problems efficiently than classical computers.
Speaker 4: Yes, I mean, of course that's the point, and that's
Speaker 4: where this is ultimately headed. But we have to be
Speaker 4: very careful about it, because if you take these three
Speaker 4: cubits and you measure.
Speaker 5: Them, don't see those eight numbers.
Speaker 4: Now, the cubits collapse and you just see three bits each,
Speaker 4: you know, with some probability. Right now, if I have
Speaker 4: one thousand cubits, right, then that's two to the thousand
Speaker 4: power amplitudes to keep track of their state, right, which is,
Speaker 4: you know, more than you could write down in the
Speaker 4: whole observable universe. Okay, so there seems to be that
Speaker 4: this exponentiality, you know, beneath the surface. And this is
Speaker 4: certainly a problem if you wanted to simulate quantum mechanics
Speaker 4: on a conventional computer. Okay. And actually chemists and physicists
Speaker 4: have known that for generations, right. They're like, once they
Speaker 4: started trying to apply the Schrodinger equation to you know,
Speaker 4: calculate the behavior of even quite simple molecules, right, they
Speaker 4: have to write down a wave function that has like
Speaker 4: more and more dimensions, you know, the more electrons you add, right,
Speaker 4: and this you very quickly get the problems that would
Speaker 4: tax you know, the fastest supercomputers of today. You know,
Speaker 4: let alone of the nineteen fifties when they started doing this, right,
Speaker 4: And so you know, chemists and physicists invented all sorts
Speaker 4: of hacks and approximation methods for dealing with these exponentially
Speaker 4: large wave functions. Okay, But it was not until the
Speaker 4: early eighties that a few physicists like Feineman and Deutsch,
Speaker 4: you know, started saying, well, if nature is giving us
Speaker 4: this computational lemon, you know, it is making it so
Speaker 4: hard for us to simulate atomic physics on computers.
Speaker 5: Then why don't we make lemonade? That is, you know,
Speaker 5: why don't we build.
Speaker 4: A computer that itself would exploit quantum mechanical principles, you
Speaker 4: know what they called a quantum computer.
Speaker 5: And then what would that be good for?
Speaker 4: Well, if nothing else, it would be good for simulating
Speaker 4: quantum mechanics itself, right, And that was sort of the
Speaker 4: original application that they had in mind, And forty years later,
Speaker 4: I think that that's still, honestly, you know, the most
Speaker 4: important application economic that we know. A bet that's the
Speaker 4: truth of the matter, right.
Speaker 5: Anyone who is.
Speaker 4: Trying to design better batteries or better solar cells, or
Speaker 4: high temperature superconductors, or better chemical reactions for making fertilizer
Speaker 4: or better drugs that you know buind a receptor in
Speaker 4: a certain way, right, they're basically dealing with a many
Speaker 4: body quantum mechanics problem. And these problems can be incredibly
Speaker 4: hard for classical computers for reasons that you know, ultimately
Speaker 4: come from the exponentiality of the wave function, right, and
Speaker 4: a quantum computer could potentially help with any of that.
Speaker 1: Scott is pointing out a really crucial feature of quantum systems.
Speaker 1: Not only do they have these cubits that can be
Speaker 1: in superposition, but the cubits can be entangled with each other,
Speaker 1: which means the value in one bit can be linked
Speaker 1: to the value in another bit, and that's what makes
Speaker 1: them much more information dense than classical computers. It's like,
Speaker 1: instead of having three independent axes where you can just
Speaker 1: pick a number along the access, you have three pieces
Speaker 1: of information. You semble those into a three dimensional space,
Speaker 1: so now you have a three D volume of information.
Speaker 1: So they're capable of storing information much more densely because
Speaker 1: of these connections between the bits, which creates this information space,
Speaker 1: and this makes it very hard for classical computers to
Speaker 1: simulate a quantum system. It takes a lot of normal bits,
Speaker 1: classical zero one bits to calculate what a quantum system
Speaker 1: will do or what a cbe bit will do. So
Speaker 1: the first thing that quantum computers could be good for
Speaker 1: is to just describe quantum systems. It's kind of a
Speaker 1: natural application. You know, the quantum computer follows similar rules
Speaker 1: to the quantum system, and so it's natural to describe
Speaker 1: it in that way, the same way like the baseball
Speaker 1: follows the rules of the baseball. So it's a good
Speaker 1: way to calculate what a baseball will do. But of
Speaker 1: course we wonder, like, is that all quantum computers can
Speaker 1: do simulate some nerdy quantum experiment or are quantum computers
Speaker 1: also good at doing other things? Here's Scott telling us
Speaker 1: all about it.
Speaker 5: That was the original promise.
Speaker 4: But you know, as long as that was sort of
Speaker 4: the only promise, I think, you know, this remained very
Speaker 4: much a niche interest of some weird physicists pursuing this idea.
Speaker 5: And you know, the eighties, the early nineties.
Speaker 4: Now, the big discovery that put quantum computing on the
Speaker 4: map for you know, most of the rest of the
Speaker 4: world was that a quantum computer can sometimes also help
Speaker 4: to get exponential speed ups, even for problems that have
Speaker 4: nothing to do with quantum mechanics, at least for a
Speaker 4: few very specific such problems.
Speaker 1: Can we predict these kinds of problems in advance?
Speaker 4: Welcome to what my colleagues and I have been trying
Speaker 4: to do for the last thirty years.
Speaker 5: Yeah, I mean, for the whole history of this field.
Speaker 4: We are trying to figure out what is the border
Speaker 4: between what is efficiently solvable by a quantum computer and
Speaker 4: what isn't and we know a lot about it, but
Speaker 4: you know, there is.
Speaker 5: A great deal that we still don't know.
Speaker 4: The big discovery that sort of started quantum computing as
Speaker 4: a field, I would say, you know, as opposed to
Speaker 4: just an idea, came in nineteen ninety four, Okay, and
Speaker 4: that was when Peter Shore, who was a mathematician then
Speaker 4: at Belle Ebs, discovered that there is a fast quantum
Speaker 4: algorithm for factoring numbers.
Speaker 5: Okay, So he discovered that the factoring.
Speaker 4: Problem, the problem of factoring a huge composite number into primes,
Speaker 4: and some various closely related problems of central importance in
Speaker 4: modern cryptography are all solvable on a quantum computer using
Speaker 4: a number of steps that grows like the size of
Speaker 4: the number. You know that you're trying to factor squared maybe,
Speaker 4: but not exponentially with the size.
Speaker 2: Of the number.
Speaker 1: So this is a big deal because, as you're saying,
Speaker 1: you know, we can use billiard balls to calculate how
Speaker 1: billiard balls move, and we can use quantum systems to
Speaker 1: simulate quantum systems. But now we're using a quotum system
Speaker 1: to describe something that's fundamentally not quantum. So it gives
Speaker 1: us a clue that like, maybe we can open up
Speaker 1: a whole new category of problems.
Speaker 4: It is totally not obvious a priori that a quantum
Speaker 4: computer should help you for factoring numbers.
Speaker 5: You know, what does that have to do with quantum mechanics?
Speaker 4: Right? And of course this problem is hugely important because,
Speaker 4: for better or worse, we base the whole security of
Speaker 4: the modern Internet on the belief that factoring is hard. Okay,
Speaker 4: What Sure was saying is that if and when someone
Speaker 4: builds a large quantum computer, a scalable quantum computer with
Speaker 4: you know, thousands or millions of cubits, then that is
Speaker 4: no longer true. Okay, then you can break all of
Speaker 4: the encryption that we use to protect the Internet.
Speaker 5: So a bunch of things happened, you know after that.
Speaker 4: You know, one was people you know kind of like
Speaker 4: with the story of Rumpelstiltskin, Right, It's like, if you
Speaker 4: can spin this much straw into gold, and then why
Speaker 4: not more? And people said, well, maybe all of the exponential,
Speaker 4: really hard problems that we're you know, dealing with, maybe
Speaker 4: quantum computers can solve all of them.
Speaker 1: Is there any way to intuitively understand the idea here?
Speaker 1: Like what it is about quantum computing that makes this
Speaker 1: problem easier to do.
Speaker 4: I teach a whole undergrad course where you know, at
Speaker 4: the end of it, I hope that people will have
Speaker 4: the intuition for these things.
Speaker 8: Right.
Speaker 4: But like, if there were a one sentence way to
Speaker 4: say the intuition, then you wouldn't have needed Shore and
Speaker 4: Grover to discover these things, right, you know, it could
Speaker 4: have been obvious from the beginning.
Speaker 5: Right. But let me say this, right, so, you know.
Speaker 4: What almost every popular article about quantum computing wants to
Speaker 4: say is something that sounds really appealing and is totally wrong. Okay.
Speaker 4: In fact, Zach Kelly's husband and I made a whole
Speaker 4: cartoon about exactly this eight years ago.
Speaker 1: So throw cold water on some clickbait for us. What's
Speaker 1: wrong about quantum computing descriptions?
Speaker 4: What almost every popular writer has wanted to say is
Speaker 4: that a quantum computer just tries every possible solution in parallel.
Speaker 4: You know, it tries each one in a different parallel universe,
Speaker 4: or you know, all of them in superposition or whatever,
Speaker 4: and then somehow magically the best one gets picked. Right.
Speaker 4: If that were how it worked, then quantum computers would solve,
Speaker 4: not only factoring, but also np complete problems. Right, they
Speaker 4: would break not only the cryptosystems that currently protect the Internet,
Speaker 4: but they would break all other possible cryptosystems you know
Speaker 4: that are based on hard problems. Okay, but that is
Speaker 4: not what we believe that quantum computers can do, right.
Speaker 4: We believe that they're more limited than that. So the
Speaker 4: question is why are they more limited? Okay?
Speaker 5: And it all has to do with the restrictions of measurement.
Speaker 2: Right.
Speaker 4: It's true that with a quantum computer you can create
Speaker 4: an equal superposition over all the possible solutions to your
Speaker 4: hard problem. That's even an easy thing to do if
Speaker 4: you have a quantum computer, right, like create a superposition
Speaker 4: or each of these two to the thousand and power
Speaker 4: possible solutions has some amplitude. You know, that's just like
Speaker 4: very simple, it's done, Okay. The trouble is for a
Speaker 4: computer to be useful, at some point you have to
Speaker 4: look at it. You have to measure, you have to
Speaker 4: get an output. You know, you have to read something out. Okay.
Speaker 4: And if you took an equal superposition over all the
Speaker 4: answers and you just measured it, not having done anything else,
Speaker 4: then the rules of quantum mechanics are very clear on
Speaker 4: what you're going to see. It's a completely random answer.
Speaker 4: And if you had just wanted a completely random answer,
Speaker 4: then you could have just flipped a coin a bunch
Speaker 4: of times, or just you know, use a random number
Speaker 4: generator that's inside your classical computer. Right, You didn't need
Speaker 4: to spend all these billions of dollars to build.
Speaker 5: A quantum computer.
Speaker 4: Right, So the only hope of getting an advantage from
Speaker 4: a quantum computer is to exploit the way that these amplitudes,
Speaker 4: you know, being complex numbers, work differently from conventional probabilities. Okay,
Speaker 4: And the central thing that amplitudes can do that probabilities
Speaker 4: cannot do is that they can interfere with each other.
Speaker 4: They can cancel each other out. And so with a
Speaker 4: quantum computer in particular, the idea with every algorithm for
Speaker 4: a quantum computer is that you are trying to choreograph
Speaker 4: things in such a way that for each wrong answer,
Speaker 4: because each answer that you don't want to see, some
Speaker 4: contributions to its amplitude are positive and others are negative,
Speaker 4: so they're canceling each other out.
Speaker 5: Whereas for the.
Speaker 4: Right answer, the answer you do one you want all
Speaker 4: the contributions to its amplitude to reinforce each other, okay,
Speaker 4: to add up constructively. If you can arrange that, then
Speaker 4: when you look, you're going to see the right answer
Speaker 4: with a large probability.
Speaker 5: That's the name of the game.
Speaker 4: Now. The hard part is you have to do all
Speaker 4: of that even though you yourself don't know in advance
Speaker 4: which answer is the right one.
Speaker 5: You know, if you already knew, what would be the point?
Speaker 4: Right, And you have to do all of this faster
Speaker 4: than even the cleverest classical algorithm could do the same thing.
Speaker 5: Otherwise what would be the point?
Speaker 4: Okay, so nature is giving us this really really bizarre
Speaker 4: hammer and a priori.
Speaker 5: It's not obvious whether.
Speaker 4: There's any nails that that hammer can hit, you know,
Speaker 4: other than just the obvious one of simulating quantum physics itself, right,
Speaker 4: And it really it took more than a decade for
Speaker 4: people to discover those nails and factoring the problem that
Speaker 4: Peter Shore designed his algorithm for. That was the first
Speaker 4: big example, and some people hope that that would be
Speaker 4: followed by a flood of other examples, and you know, unfortunately,
Speaker 4: you know, thirty years later, factoring remains one of our
Speaker 4: pre eminent examples.
Speaker 1: So I have a crazy question for you. You're telling
Speaker 1: me that quantum computers work by maintaining all of these
Speaker 1: different amplitudes and the superpositions, but that we don't have
Speaker 1: access to all the superpositions because we have to take
Speaker 1: the measurement. So we have to play clever games with
Speaker 1: interference so that we can use this system to do
Speaker 1: something useful. But we don't have access to the superpositions
Speaker 1: because of the measurement, only because we are classical objects
Speaker 1: and classical interactions with quantum systems collapse the measurement. What
Speaker 1: if we were tiny, we were microscopic, we were quantum,
Speaker 1: could then we use quantum systems in a way that
Speaker 1: didn't collapse all their wave functions and access all of
Speaker 1: those amplitudes.
Speaker 4: So I hate to break it to you, us being
Speaker 4: microscopic wouldn't help. Right, You can be as tiny as
Speaker 4: you like, But if you interact with a quantum system
Speaker 4: in a way that carries away the information about you know,
Speaker 4: which branch of the superposition we're we in, then that
Speaker 4: has exactly the same effect of collapsing the state. Right,
Speaker 4: So it's not our physical bigness that's the issue. It's that,
Speaker 4: you know, sort of we want an answer in our universe, right,
Speaker 4: what we can say, You know, there are people who
Speaker 4: are very gung ho about the interpretation of quantum mechanics,
Speaker 4: where they would say, collapse is not real, right, collapse
Speaker 4: is just a figment of our limited perspective. Right. Really,
Speaker 4: what's going on is it's just the Schrodinger equation all
Speaker 4: the way. And so they would say that whenever you know,
Speaker 4: you measure a quantum computer that's in a superposition, Actually
Speaker 4: the whole universe then splits into all these different branches,
Speaker 4: and each branch is equally real.
Speaker 5: We have an experience of one of them where.
Speaker 4: We perceive some answer. So a many worlder, that's what
Speaker 4: these people are called, right, a Mani worlder would say
Speaker 4: that there is some branch of the universal wave function
Speaker 4: in which you do get the right answer right, in
Speaker 4: which you try all the possible answers in superposition. You know,
Speaker 4: they would say, well, there's some branch where you get
Speaker 4: lucky and you measure the right answer, right. And they love,
Speaker 4: you know, all sorts of crazy thought experiments, like you know,
Speaker 4: quantum suicide, right, Like why not.
Speaker 5: Use a quantum random number?
Speaker 4: Generator to pick a lottery ticket and then just decide
Speaker 4: to kill yourself if it doesn't end up being winning,
Speaker 4: and then in all the branches of the wave function
Speaker 4: where you still.
Speaker 5: Exist, you know you'll have won the lottery.
Speaker 4: Now, I do not recommend that any listeners try that, Okay.
Speaker 4: I don't think there's any principle of reason that says,
Speaker 4: you know, you get to condition on being in a
Speaker 4: branch of the wave function where you're alive.
Speaker 1: Yeah, it sounds like a cool premise for a streaming show,
Speaker 1: but not a way to live your life.
Speaker 4: That is the one thing that I can say in
Speaker 4: the direction of what you were hoping for, and it's
Speaker 4: not very useful, I'm afraid.
Speaker 2: All right.
Speaker 1: So you're sort of famous for, you know, throwing cold
Speaker 1: water on mis explanations of quantum computing and maybe even overhype.
Speaker 1: Let me flip that around and ask you what is
Speaker 1: the most under hyped aspect of quantum computing. What is
Speaker 1: the thing that you're most excited about that would make
Speaker 1: you like, take your family money and invest it in
Speaker 1: somebody's quantum startup if you heard them doing it, if
Speaker 1: I had that.
Speaker 5: Kind of risk tolerance.
Speaker 4: There are so many things that I knew about when
Speaker 4: they were small, that I should have been investing in.
Speaker 4: And you know, clearly it's probably for the best that
Speaker 4: I became a professor and not an investor. I mean,
Speaker 4: one thing I think that a lot of people don't
Speaker 4: realize is that, you know, we are just within the
Speaker 4: last year the experimentalists have gotten very very close to
Speaker 4: the degree of control, you know, over cubits that would
Speaker 4: be needed to build a scalable quantum computer with with
Speaker 4: what we call quantum error correction, which is the technology
Speaker 4: that would ultimately allow you to sort of keep your
Speaker 4: cubits isolated, keep them from being prematurely measured by their environment,
Speaker 4: and you know, do an arbitrarily long quantum come computation
Speaker 4: with them. Right, So people have been talking about these
Speaker 4: ideas for thirty years now, and you know, so some
Speaker 4: people you may have gotten fatigue with quantum computing. What
Speaker 4: we've known since the mid nineteen nineties is that if
Speaker 4: you can control two pairs of cubits well enough, like
Speaker 4: if you can get the error the noise in a
Speaker 4: two cubit interaction to be sufficiently small, then there are
Speaker 4: these very very clever quantum error correcting codes that can
Speaker 4: get you the rest of the way that can encode
Speaker 4: like a single logical cubit across an entangled state of
Speaker 4: tens or hundreds of physical cubits, you know, in such
Speaker 4: a way that you can survive and recover from, you know,
Speaker 4: an error on any one of.
Speaker 5: The physical cubits.
Speaker 4: And you know, these codes have the effect of pushing
Speaker 4: your effective error rate down closer and closer to zero. Right,
Speaker 4: but only after you've passed this critical point at which
Speaker 4: the error correction becomes a net win, which it starts
Speaker 4: making things better rather than making them worse. It's almost
Speaker 4: as if like you have to pass the critical mass
Speaker 4: for a nuclear reaction. Right, if you're halfway there, you
Speaker 4: don't get half the reaction, right, you know, you need
Speaker 4: to pass criticality, okay, And so that's sort of been
Speaker 4: the engineering goal of the people who, unlike me, have
Speaker 4: labs and not just blackboards, right, who are actually trying
Speaker 4: to build these devices. You know, that's been their goal
Speaker 4: for thirty years, and I think you know, many people
Speaker 4: might not realize just how close they are. So basically,
Speaker 4: you know, the estimate is that you want to be
Speaker 4: able to, you know, apply a two cubit gait, that is,
Speaker 4: you know, do a desired operation on two cubits with
Speaker 4: about ninety nine point nine nine percent accuracy, about four
Speaker 4: nines of accuracy, and that ought to be enough to
Speaker 4: get this quantum error correction, you know, self sustaining reaction started.
Speaker 4: And when I joined the field, which was in the
Speaker 4: late nineteen nineties, such as a few years after shores
Speaker 4: and grovers algorithms had been discovered, right, it would have
Speaker 4: been amazing if you could do like a two cubit
Speaker 4: gate with fifty percent accuracy, right, Like that would have
Speaker 4: been a nature paper, okay. But then at some point
Speaker 4: the fifty percent became ninety percent, and then that became
Speaker 4: ninety five ninety nine percent, and within the last year
Speaker 4: we've seen like ninety nine point nine percent accuracy, okay,
Speaker 4: in several different groups. You know, the neutral atoms grew
Speaker 4: Querra and Boston, the trapped ions which like Quantinuum in
Speaker 4: Colorado does superconducting cubits, which at Google and IBM are doing.
Speaker 4: So you know, so a bunch of different approaches are
Speaker 4: you know, being pursued simultaneously, but you know, several of
Speaker 4: them are getting up to this, like ninety nine point
Speaker 4: nine percent accuracy, and it looks like just one more
Speaker 4: nine and you should be at the point where you
Speaker 4: know this self sustaining reaction works. So I think that's
Speaker 4: the central case for optimism right now. For just looking
Speaker 4: at the hour raid as a function of year, it
Speaker 4: looks like either you get there in the next decade
Speaker 4: or else something surprising.
Speaker 5: Happens, you know that explains why you didn't get there.
Speaker 4: That's one aspect of the story that maybe is not
Speaker 4: so well appreciated.
Speaker 1: All right, So that was our interview with Scott in
Speaker 1: conversation about quantum computing. I think the answer to the
Speaker 1: question should you be excited about quantum computing is yes.
Speaker 1: This potentially represents a whole new era of computing computers
Speaker 1: that are good at solving different kinds of problems than
Speaker 1: our traditional computers and opens our minds to the way
Speaker 1: computation can be done. Maybe there are other kinds of computers,
Speaker 1: not just classical or quantum computers, but other kind of things.
Speaker 1: Ways we can take advantage of the universe to do
Speaker 1: the calculations that we want to do. Thanks Scott very
Speaker 1: much for joining us, and thank you to everybody for listening.
Speaker 1: Tune in next time.
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