Interview on quantum computation with
用量子计算访谈量子计算Scott Aaronson, theoretical computer scientist and David J. Bruton Jr. Centennial Professor of Computer Science at the University of Texas at Austin. His primary areas of research are quantum computing and computational complexity theory.
,理论计算机科学家和David J. Bruton Jr.德克萨斯大学奥斯汀分校计算机科学百年教授。他的主要研究领域是量子计算和计算复杂性理论。
Scott blogged about this and other segments of our interview -- his blog is very popular and has way more comments than this one does --
和我们采访的其他部分 - 他的博客非常受欢迎,并且有比这更多的评论 -check it out.


Scott Aaronson: Okay so -- Hi, I'm Scott Aaronson. I'm a computer science professor at the University of Texas at Austin and my main interest is the capabilities and limits of quantum computers, and more broadly what computer science and physics have to tell each other. And I got interested in it I guess because it was hard not to be -- because as a teenager it just seemed clear to me that the universe is a giant video game and it just obeys certain rules, and so if I really wanted to understand the universe maybe I could ignore the details of physics and just think about computation.
好的 - 嗨,我是Scott Aaronson。我是德克萨斯大学奥斯汀分校的计算机科学教授,我的主要兴趣是量子计算机的能力和极限,更广泛地说是计算机科学和物理学必须互相告知的。而且我对此感兴趣,因为它很难不成为 - 因为作为一个少年,我似乎很清楚,宇宙是一个巨大的视频游戏,它只是服从某些规则,所以如果我真的想了解宇宙也许我可以忽略物理学的细节,只考虑计算。
But then with the birth of quantum computing and the dramatic discoveries in the mid-1990s (like Shor's algorithm for factoring huge numbers) it became clear that physics actually changes the basic rules of computation -- so that was something that I felt like I had to understand. And 20 years later we're still trying to understand it, and we may also be able to build some devices that can outperform classical computers namely quantum computers and use them to do some interesting things.
但随着量子计算的诞生和20世纪90年代中期的戏剧性发现(如Shor的算法,可以算出巨大的数字),很明显物理实际上改变了计算的基本规则 - 所以这就像我觉得的那样了解。 20年后,我们仍然试图理解它,我们也可以构建一些能够胜过经典计算机的设备,即量子计算机,并用它们做一些有趣的事情。
But to me that's that's really just icing on the cake; really I just want to understand how things fit together. Well to tell you the truth when I first heard about quantum computing (I think from reading some popular article in the mid 90s about Shor's algorithm which had only recently been discovered) my first reaction was this sounds like obvious hogwash; this sounds like some physicists who just do not understand the first thing about computation -- and they're just inventing some physics proposal that sounds like it just tries every possible solution in parallel. But none of these things are going to scale and in computer science there's been decades of experience of that; of people saying: well why don't you build a computer using a bunch of mirrors? or using soap bubbles? or using folding proteins?
但对我来说,这真的只是锦上添花;我真的只想了解事情是如何融合在一起的。好吧,当我第一次听说量子计算的时候说实话(我想从90年代中期读到一些关于Shor的算法的流行文章,这个文章最近才被发现),我的第一反应是听起来像是明显的h ;;这听起来像是一些物理学家,他们只是不了解计算的第一件事 - 而且他们只是发明了一些物理建议,听起来它只是并行地尝试了所有可能的解决方案。但是这些东西都不会扩展,而在计算机科学领域,已有数十年的经验;人们说:那你为什么不用一堆镜子建造一台电脑呢?还是用肥皂泡?或使用折叠蛋白?
And there's all kinds of ideas that on paper look like they could evaluate an exponential number of solutions at only a linear amount of time, but they're always kind of idealizing something? So it's always when you examine them carefully enough you find that the amount of energy or scales explose up on you exponentially, or the precision with which you would need to measure becomes exponentially precise, or something becomes totally unrealistic -- and I thought the same must be true of quantum computing. But in order to be sure I had to read something about it.
在纸面上有各种各样的想法,他们可以在一段线性的时间内评估指数数量的解决方案,但它们总是有点理想化的东西?因此,当你仔细检查它们时,你会发现能量或量表的数量会以指数方式向你发展,或者你需要测量的精确度会呈指数精确,或者某些东西变得完全不切实际 - 我认为同样的必须适用于量子计算。但为了确保我必须阅读有关它的一些内容。
So I while I was working over a summer at Bell Labs doing work that had nothing to do with quantum computing, well my boss was nice enough to let me spend some time learning about and reading up on the basics of quantum computing -- and that was really a revelation for me because I accepted [that] quantum mechanics is the real thing. It is a thing of comparable enormity to the basic principles of computation -- you can say the principles of Turing -- and it is exactly the kind of thing that could modify some of those principles. But the biggest surprise of all I think was that I despite not being a physicist not having any skill that partial differential equations or the others tools of the physicists that I could actually understand something about quantum mechanics.
所以,当我在贝尔实验室工作一个夏天,从事与量子计算无关的工作时,我的老板非常好,让我花一些时间学习和阅读量子计算的基础知识 - 那就是对我来说真的是一个启示,因为我接受量子力学是真实的。它与计算的基本原理具有相当的重要性 - 你可以说图灵的原理 - 它正是可以修改其中一些原则的东西。但我认为最令人惊讶的是,尽管我不是一个没有任何技能的物理学家,但他不具备偏微分方程或物理学家的其他工具,我实际上可以理解量子力学。
And ultimately to learn the basic rules of how a quantum computer would work and start thinking about what they would be good for -- quantum algorithms and things like that -- it's enough to be conversant with vectors and matrice. So you need to know a little bit of math but not that much. You need to be able to know linear algebra okay and that's about it.
最终要学习量子计算机如何工作的基本规则,并开始考虑它们有什么用处 - 量子算法和类似的东西 - 它足以熟悉向量和矩阵。所以你需要知道一些数学但不是那么多。你需要能够知道线性代数,这就是它。
And I feel like this is a kind of a secret that gets buried in almost all the popular articles; they make it sound like quantum mechanics is just this endless profusion of counterintuitive things. That it's: particles can be in two places at once, and a cat can be both dead and alive until you look at it, and then why is that not just a fancy way of saying well either the cat's alive or dead and you don't know which one until you look -- they they never quite explained that part, and particles can have spooky action at a distance and affect each other instantaneously, and particles can tunnel through walls! It all sounds hopelessly obscure and you know there's no hope for anyone who's not a PhD in physics to understand any of it.
而且我觉得这是一种隐藏在几乎所有热门文章中的秘密;它们听起来像量子力学就是这种无意义的反直觉的东西。它是:粒子可以同时在两个地方,一只猫可以既死又活着,直到你看到它,然后为什么这不仅仅是一个好看的方式说好猫还活着还是死了你不要知道哪一个直到你看 - 他们从来没有完全解释那个部分,并且粒子可以在远处产生怪异的动作并瞬间相互影响,并且粒子可以穿过墙壁!这一切听起来毫无希望地晦涩难懂,你知道任何不是物理学博士学位的人都无法理解它。
But the truth of the matter is there's this one counterintuitive hump that you have to get over which is the certain change to or generalization of the rules of probability -- and once you've gotten that then all the other things are just different ways of talking about or different manifestations of that one change. And a quantum computer in particular is just a computer that tries to take advantage of this one change to the rules of probability that the physicists discovered in the 1920s was needed to account for our world. And so that was really a revelation for me -- that even you're computer scientists are math people; people who are not physicists can actually learn this and start contributing to it -- yeah!
但问题的真相是,这是一个违反直觉的驼峰,你必须克服哪些是概率规则的某种变化或概括 - 一旦你得到了那么所有其他事情只是不同的方式谈论那个变化的不同表现形式。特别是量子计算机只是一台计算机,它试图利用物理学家在20世纪20年代发现的概率规则的这一变化来解释我们的世界。所以这对我来说真的是一个启示 - 即使你是计算机科学家也是数学人;那些不是物理学家的人实际上可以学习这一点并开始为此做出贡献 - 是的!

Adam Ford: So it's interesting that often when you try to pursue an idea, the practical gets in the way -- we try to get to the ideal without actually considering the practical -- and they feel like enemies. Should we be letting the ideal be the enemy of the practical?
所以有趣的是,当你试图追求一个想法时,实践会妨碍我们 - 我们试图在不考虑实际的情况下达到理想 - 他们感觉自己就像敌人一样。我们应该让理想成为实际的敌人吗?

Scott Aaronson : Well I think that from the very beginning it was clear that there is a theoretical branch of quantum computing which is where you just assume you have as many of these quantum bits (qubits) as you could possibly need, and they're perfect; they stay perfectly isolated from their environment, and you can do whatever local operations on them you might like, and then you just study how many operations would you need to factor a number, or solve some other problem of practical importance. And the theoretical branch is really the branch where I started out in this field and where I've mostly been ever since.
And then there's the practical branch which asks well what will it take to actually build a device that instantiates this theory -- where we have to have qubits that are actually the energy levels of an electron, or the spin states of an atomic nucleus, or are otherwise somehow instantiated in the physical world. And they will be noisy, they will be interacting with their environment -- we will have to take heroic efforts to keep them sufficiently isolated from their environments -- which is needed in order to maintain their superposition state. How do we do that?
然后是实用的分支,它要求实际构建一个实例化该理论的器件需要什么 - 我们必须有量子位,实际上是电子的能级,或原子核的自旋态,或者否则以某种方式在物理世界中实例化。他们会吵闹,他们将与他们的环境互动 - 我们将不得不采取英勇的努力,使他们与他们的环境保持足够的隔离 - 这是维持他们的叠加状态所必需的。我们怎么做?
Well we're gonna need some kind of fancy error correcting codes to do that, and then there are there are theoretical questions there as well but how do you design those correcting codes?
But there's also practical questions: how do you engineer a system where the error rates are low enough that these codes can even be used at all; that if you try to apply them you won't simply be creating even more error than you're fixing. What should be the physical basis for qubits? Should it be superconducting coils? Should it be ions trapped in a magnetic field? Should it be photons? Should it be some new topological state of matter? Actually all four of those proposals and many others are all being pursued now!
So I would say that until fairly recently in the field, like five years ago or so, the theoretical and the practical branches we're pretty disjointed from each other; they were never enemies so to speak. I mean we might poke fun at each other sometimes but we were we were never enemies. The the field always sort of rose or fell as a whole and we all knew that. But we just didn't have a whole lot to scientifically say to each other because the experimentalists we're just trying to get one or two qubits to work well, and they couldn't even do that much, and we theorists we're thinking about -- well suppose you've got a billion cubits, or some arbitrary number, what could you do? And what would still be hard to do even then?
所以我要说,直到最近这个领域,就像五年前左右一样,理论和实践的分支我们彼此相互脱节;可以这么说,他们永远不是敌人。我的意思是我们有时会互相嘲笑,但我们从来都不是敌人。整个领域总体上升或下降,我们都知道。但是我们只是没有很多东西可以科学地互相说出来,因为实验主义者我们只是试图让一两个量子比特工作得很好,他们甚至做不到那么多,我们理论家就是思考 - 假设你有十亿肘或一些任意数字,你能做什么?即便如此,还有什么难以做到的?
A lot of my work was has actually been about the limitations of quantum computers, but I also like to say the study of what you can't do even with computers that you don't have. And only recently the experimentalists have finally gotten the qubits to work pretty well in isolation so that now it finally makes sense to start to scale things up -- not yet to a million qubits but maybe 50 qubits, maybe to 60, maybe to a hundred. This as it happens is what
我的很多工作实际上都是关于量子计算机的局限性,但我也想说研究一下即使使用没有的计算机也无法做到的事情。而且直到最近,实验主义者才最终使量子比特在隔离状态下工作得很好,所以现在最终开始扩大规模是有道理的 - 还不到百万个量子比特但可能是50个量子比特,可能是60个,也许是一百个。这恰好是什么
Google and IBM and Intel and a bunch of startup companies are trying to do right now. And some of them are hoping to have devices within the next year or two, that might or might not do anything useful but if all goes well we hope will at least be able to do something interesting -- in the sense of something that would be challenging for a classical computer to simulate, and that at least proves the point that we can do something this way that is beyond what classical computers can do.
谷歌,IBM和英特尔以及一些创业公司正在努力做到这一点。他们中的一些人希望在未来一两年内拥有设备,可能会或可能不会做任何有用的事情,但如果一切顺利,我们希望至少能够做一些有趣的事情 - 从某种意义上说挑战古典计算机进行模拟,至少证明了我们可以用这种方式做的事情超出了传统计算机的作用。
And so as a result the most nitty-gritty experimentalists are now actually talking to us theorists because now they need to know -- not just as a matter of intellectual curiosity, but as a fairly pressing practical matter -- once we get 50 or 100 cubits working what do we do with them? What do we do with them first of all that you know is hard to simulate classically? How sure are you that there's no fast classical method to do the same thing? How do we verify that we've really done it , and is it useful
因此,大多数细节实验主义者现在实际上正在与我们的理论家交谈,因为现在他们需要知道 - 不仅仅是作为一种求知欲,而是一个相当紧迫的实际问题 - 一旦我们得到50或100肘部工作我们用它们做什么?我们如何处理它们首先你知道难以经典地模拟?你有多确定没有快速的经典方法来做同样的事情?我们如何验证我们是否真的完成了它,并且它是否有用
for anything?
for anything?
And ideally they would like us to come up with proposals that actually fit the constraints of the hardware that they're building, where you could say you know eventually none of this should matter, eventually a quantum programmer should be able to pay as little attention to the hardware as a classical programmer has to worry about the details of the transistors today.
But in the near future when we only have 50 or 100 cubits you're gonna have to make the maximum use of each and every qubit that you've got, and the actual details of the hardware are going to matter, and the result is that even we theorists have had to learn about these details in a way that we didn't before.
There's been a sort of coming together of the theory and practical branches of the field just in the last few years that to me has been pretty exciting.

Adam Ford : So you think we will have something equivalent to functional programming for quantum computing in the near future?

Scott Aaronson : Well there actually has been a fair amount of work on the design of quantum programming languages. There's actually a bunch of them out there now that you can download and try out if you'd like. There's one called Quipper, there's another one called a Q# from Microsoft, and there are several others. Of course we don't yet have very good hardware to run the programs on yet, mostly you can just run them in classical simulation, which naturally only works well for up to about 30 or 40 cubits, and then it becomes too slow. But if you would like to get some experience with quantum programming you can try these things out today, and many of them do try to provide higher level functionalities, so that you're not just doing the quantum analog of assembly language programming, but you can think in higher-level modules, or you can program functionally. I would say that in quantum algorithms we've mostly just been doing theory and we haven't been implementing anything, but we have had to learn to think that way. If we had to think in terms of each individual qubit, each individual operation on one or two
qubits, well we would never get very far right? And so we have to think in higher-level terms like there are certain modules that we know can be done -- one of them is called the Quantum Fourier Transform and that's actually the heart of Shor's famous algorithm for factoring numbers (it has other applications as well). Another one is called Amplitude Amplification that's the heart of Grover's famous algorithm for searching long long lists of numbers
量子比特,我们永远不会得到很远的权利?所以我们必须用更高层次的术语来思考,比如我们知道可以做的某些模块 - 其中一个称为量子傅里叶变换,这实际上是Shor着名的数字分解算法的核心(它还有其他应用)以及)。另一个被称为振幅放大,这是Grover着名的搜索长长数字列表算法的核心
in about the square root of the number of steps that you would need classically, and that's also like a quantum algorithm design primitive that we can just kind of plug in as a black box and it has many applications.
So we do think in these higher level terms, but there's a different set of higher level abstractions than there would be for classical computing -- and so you have to learn those. But the basic idea of decomposing a complicated
所以我们在这些更高级别的术语中思考,但是有一组不同于更高级别的抽象,而不是经典计算 - 因此你必须学习这些。但分解复杂的基本思路
problem by breaking it down into its sub components that's exactly the same in quantum computing as it is in classical computing.

Adam Ford : Are you optimistic with regards to quantum computing in the short to medium term?

Scott Aaronson : You're asking what am I optimistic about -- so I am I mean like I feel like the field has made amazing progress: both on theory side and on the experimental side. We're not there yet, but we know a lot more than we did a decade ago. Some of what were my favorite open problems as a theorist a decade ago have now been resolved -- some of them within the last year -- actually and the hardware the qubits are not yet good enough to build a scalable quantum computer -- in that sense the skeptics can clearly legitimately say we're not there yet -- well no duh we're not -- okay but: if you look at the coherence times of the qubits, you look at what you can do with them, and you compare that to where they were 10 years ago or 20 years ago -- there's been orders of magnitude type of progress. So the analogy that I like to make: Charles Babbage laid down the basic principles of classical computing in the 1820s right? I mean not with as much mathematical rigor as Turing would do later, but the basic ideas were there. He had what today we would call a design for a universal computer.
:你在问我对什么是乐观的 - 所以我的意思是我觉得这个领域取得了惊人的进步:无论是在理论方面还是在实验方面。我们还没有,但我们比十年前知道得多。十年前作为一个理论家,我最喜欢的一些开放性问题现在已经得到解决 - 其中一些在去年内 - 实际上和量子比特的硬件还不足以构建一个可扩展的量子计算机 - 感觉怀疑者可以清楚地合法地说我们还没有 - 好吧没有,我们不是 - 好吧但是:如果你看看量子比特的连贯时间,你会看看你能用它们做什么,而你将它与10年前或20年前的情况进行比较 - 已经达到了数量级的进步。所以我喜欢做的类比:Charles Babbage在19世纪20年代制定了经典计算的基本原理吗?我的意思不是像图灵以后那样严格的数学严谨,但基本的想法就在那里。他今天有一个我们称之为通用计算机设计的东西。
So now imagine someone then saying 'well so when is this analytical engine gonna get built? will it be in the 1830s or will it take all the way until the 1840s?' Well in this case it took more than a hundred years for a technology to be invented -- namely the transistor -- that really fully realized Babbage's vision. I mean the vacuum tube came along earlier, and you could say partially realized that but it was just not reliable enough to really be scalable in the way that the transistor was. And optimistically now we're in the very very early vacuum tube era of quantum computing. We don't yet have the quantum computing analog of the transistor as people don't even agree about which technology is the right one to scale up yet. Is it superconducting? Is it trapped ions? Is it photonics? Is it a topological matter? So they're pursuing all these different approaches in parallel. The partisans of each approach have what sounds like compelling arguments as to why none of the other approaches could possibly scale. I hope that they're not all correct uh-huh. People have only just recently gotten to the stage where one or two qubits work well in isolation, and where it makes sense to try to scale up to 50 or 100 of them and see if you can get them working well together at that kind of scale.
所以现在想象一下有人说'那么这个分析引擎什么时候会建成?它会在19世纪30年代还是一直持续到19世纪40年代?那么在这种情况下,发明一项技术 - 即晶体管 - 需要一百多年才真正完全实现巴贝奇的愿景。我的意思是真空管早先出现了,你可以说部分意识到这一点,但它不够可靠,不像晶体管那样真正可扩展。而且乐观地说,现在我们处于量子计算的早期真空管时代。我们还没有晶体管的量子计算模拟,因为人们甚至不同意哪种技术是适合扩大规模的技术。它是超导的吗?是被困的离子吗?是光子学吗?这是一个拓扑问题吗?所以他们并行追求所有这些不同的方法。每种方法的支持者都听起来像是为什么其他方法都无法扩展的令人信服的论据。我希望他们都不正确嗯嗯。人们刚才刚刚进入一个或两个量子比特孤立运行的阶段,并且尝试扩展到50或100个它们是有意义的,看看你是否可以让它们在这种规模下很好地协同工作。
And so I think the the big thing to watch for in the next five to ten years is what's been saddled with the somewhat unfortunate name of 'Quantum Supremacy' (and this was coined before Trump I hasten to say). But so this is just a term to refer to doing something with a quantum computer it's not necessarily useful but that at least is classically hard. So you know as I was saying earlier, proves the point that you can do something that would take a lot longer to simulate it with a classical computer. And this is the thing that Google and some others are going to take their best shot at within the next couple of years so. What puts that in the realm of possibility is that just a mere 50 or 100 cubits if they work well enough should already be enough to get us this. In principle you know you may be able to do this without needing error correction -- once you need error correction then that enormously multiplies the resources that you need to do even the simplest of what's called 'Fault-Tolerant Computing' might take many thousands of physical qubits, okay, even though everyone agrees that ultimately if you want to scale to realize the true promise of quantum computing -- or let's say to threaten our existing methods of cryptography -- then you're going to need this fault tolerance. But that I expect we're not gonna see in the next five to ten years.
所以我认为在接下来的五到十年里要注意的重要事情就是背负着"量子霸权"这个有点不幸的名字(这是在特朗普不得不说之前创造的)。但是,这只是一个术语,指的是用量子计算机做某事它不一定有用,但至少是经典的难度。所以你知道我之前说的那样,证明了你可以用经典的计算机做一些需要更长时间来模拟它的事情。谷歌和其他一些人将在未来几年内采取最佳措施。将其置于可能性范围内的原因是,如果它们运作良好,仅仅50或100肘应该已经足以让我们这样做了。原则上,您知道您可以在不需要纠错的情况下执行此操作 - 一旦您需要纠错,那么即使最简单的所谓"容错计算"可能需要数千个,您需要的资源也会大大增加物理量子比特,好吧,尽管每个人都同意,如果你想扩展以实现量子计算的真正承诺 - 或者说让威胁我们现有的密码学方法 - 那么你将需要这种容错。但我希望在未来五到十年内我们不会看到它。
If we do see it I mean that will be a huge shock -- as big a shock as it would be if you told someone in 1939 that there was going to be a nuclear weapon in six years. In that case there was a world war that sort of accelerated the timeline you could say from what it would otherwise be. In this case I hope there won't be a world war that accelerates this timeline. But my guess would be that if all goes well then quantum supremacy might be achievable within the next decade, and I hope that after that we could start to see some initial applications of quantum computing which will probably be some very very specialized ones; some things that we can already get with a hundred or so non-error-corrected qubits. And by necessity these are going to be very special things -- they might mostly be physics simulations or simulations of some simple chemistry problems.
如果我们确实看到它,我的意思是,这将是一个巨大的震撼 - 如果你在1939年告诉某人六年内将会有核武器,那将是一个巨大的冲击。在那种情况下,发生了一场世界大战,加速了你可以说的时间表。在这种情况下,我希望不会有一场加速这个时间表的世界大战。但我的猜测是,如果一切顺利,那么量子优势可能在未来十年内实现,我希望在那之后我们可以开始看到量子计算的一些初步应用,这可能是一些非常专业的应用;我们已经可以通过一百个非错误校正的量子比特获得的一些东西。并且必要时这些将是非常特殊的东西 - 它们可能主要是物理模拟或模拟一些简单的化学问题。
I actually have a proposed application for near-term quantum computers which is to generate cryptographically secure random numbers -- those random numbers that you could prove to a skeptic really were generated randomly -- turns out that even like a 50 or 60 qubit quantum computer should already be enough to give us that. But true scalable quantum computing the kind that could threaten cryptography and that could also speed up optimization problems and things like that -- that will probably require error correction -- and I could be pleasantly surprised . I'm not optimistic about that part becoming real on the next five to ten years, but you know since every everyone likes an optimist I guess I'll I try to be optimistic that we will take big steps in that direction and maybe even get there within my lifetime.
我实际上有一个拟议的近期量子计算机应用程序,它可以生成加密安全的随机数 - 你可以向怀疑论者证明的那些随机数真的是随机生成的 - 结果就像50或60的量子计算机一样应该已经足够给我们了。但真正可扩展的量子计算可能会威胁加密,并且还可能加速优化问题和类似的事情 - 这可能需要纠错 - 我可能会感到惊喜。在接下来的五到十年里,我对这一部分变得真实并不乐观,但你知道,因为每个人都喜欢乐观主义者,我想我会乐观地认为我们会朝这个方向迈出一大步,甚至可能得到在我的一生中。