 So good morning Maker Faire. Thank you for coming early and kicking it off with Beginner's Guide to Quantum Computing. I'm Talia Grishan. I'm a research scientist at IBM at the New York office. And I'm actually a beginner myself. I just started learning about this six months ago. So I'm here to tell you that anyone can get started learning about quantum computing. And hopefully after learning a little bit about it this morning, you'll be motivated to learn a little bit more. So I want to start with two facts. Fact number one, classical computers have enabled amazing things. The internet, they're part of how I got here. I was able to fly across the country on a plane. The electronic giraffe outside, blaring music. Classical computers have enabled just amazing things. But one of the things we don't often talk about is all the things they can't do. We talk about all the stuff they can do. There's so many things they can't. So I want to start with two examples of things classical computers are really bad at. And maybe they can solve small versions of these types of problems. But by the time the problem gets big enough to be interesting, we just run out of computing horsepower. So the first example is optimization. Optimization is I want to find the best solution to a problem among many possible solutions. So here's a picture of a table. This is the table at my wedding. It's one of many tables. But you can see 10 people around a table. You have 10 people over for dinner. How many different ways are there to configure 10 people around a table? The answer is 10 factorial. The number 10 seems so small. But 10 factorial is 3.6 million. There's 3.6 million ways to arrange 10 people for dinner. Next time you guys have dinner with 10 friends, share that fun fact with them. So when we go and do this, we consider some of the options. We make approximations, because it's the only way we're really going to end up seating people at all. But the truth of the matter is, every time I add one person to my dinner table, the number of possible configurations grows exponentially. So we can solve small versions of this problem on classical machines, but we don't solve big problems, big versions of this problem very well at all. Second example is chemistry. This is a picture of an nitrogenase enzyme. Anyone who's ever eaten food should care about this enzyme. It's an important catalyst for the creation of ammonia, which is an important component of food fertilizer, pharmaceuticals, and many other things. So this is the enzyme. I've called out three molecules, iron sulfide clusters in this enzyme of different sizes. The one on the left is four iron atoms and four sulfur atoms. Believe it or not, this is the biggest of those iron sulfide clusters we can simulate on the biggest supercomputer that we have. It's so small. Why is that the biggest molecule of these three that we can actually simulate on a classical machine? And the reason is because to actually simulate what's going on in that molecule, I have to account for every electron-electron repulsion and every attraction of the electron to the nuclei. And that number grows exponentially, the bigger the molecule. Every single electron exerts an electrostatic force on every single other electron. So when I add another one, I got to recalculate all the electron energies. So these two bigger clusters, they look so small. We can't simulate them. So there's actually many problems that have this characteristic. And what they have in common is this idea of exponential scaling. So there's a classic fable about the power of an exponential. And the fable goes, the creator of the game of chess brought the chessboard to the emperor and the emperor said, I love this game. What can I give you as a reward? And the craftsman said, okay, there's 64 squares on the chessboard. On day one, give me one grain of rice. And every day after that, double the number of grains of rice I get. So on the first day, the emperor gives him one grain of rice. The next day two, the next day four, the next day eight. And after a week, he had a teaspoon full of rice. But after a month, he had the rice production of a small country. And after the full 64 squares, it was Mount Everest of rice. So it grows really fast. The number 64 doesn't sound that big, but two to the 64 is an enormous number. So why do we think quantum computing is actually gonna allow us to solve some of these problems? We can't solve classically, right? So it boils down to two fundamentally quantum effects. One of the effects is superposition. So classical information is basically a string of zeros and ones. Everything that classical computing has enabled is boiled down to a sequence of zeros and ones. So quantum computing or quantum information has this property that the states can exist in a superposition of zero and one. So not just zero, not just one, but a superposition of zero and one. And you can also have complex superpositions of zero and one. So you start to be able to explore much richer set of states. So if one qubit can be in a superposition of two states, then two qubits can be in a superposition of four states. And three qubits can be in a superposition of eight states. So the possibility space you can explore is much more interesting and complex in quantum information. So the diagram on the right is showing you a superposition of five qubits, right? So you can be in a superposition of 32 states. So superposition is the first thing. The second thing is entanglement. This idea of entanglement is, okay, I've got two qubits and I'm entangling them together. So measuring the first qubit can tell me something about what will happen when I measure the second qubit. So entanglement is the second property that gives quantum information a really unique difference. So together, this allows us to totally change how we run algorithms, right? So take the optimization case. If I'm gonna consider 3.6 million possible ways of configuring 10 people at a table, classically I have to consider each one individually and then I have to compare them all, right? Here's how quantum computing is gonna solve that problem. You take your qubits, you go into a superposition of all the possible states, all the possible configurations, and then when you encode the problem into your quantum computer, you're applying a phase on each of the states. The phase is that kind of axis towards the center of that sphere you saw in the previous chart. You encode a phase on each of the states and when waves are in phase, the amplitudes add and when waves are out of phase, they cancel, right? So when you have noise canceling headphones, what you're doing is you're creating noise that's exactly out of phase with the noise you're trying to cancel, right? So in quantum computing, you're gonna go into a superposition of all these states. When you encode the problem onto the machine, you're applying a phase on each of the states and then you're using interference and you amplify some answers and you cancel other answers, eventually you arrive at the solution. So it's just totally different, right? You just gotta completely rethink how we're gonna solve these problems and that's kind of how quantum's gonna do it. So it's obvious why the number of qubits matter, right? If I have one qubit, I can be in two states and the more qubits I can have, I can be in a superposition of two to the n states. But another important factor is this error rate, right? I have to be able to control what's going on in the qubits. If I have really high errors and all of my operations don't work out as I expect them to, then that's not gonna really work. So we're promoting a new metric called quantum volume where we're saying, okay, if you increase the number of qubits, you can get to higher computational power but not if you have really high error rates. So we have to both move towards lower error rates and higher qubit count, okay? So how do you actually build a quantum computer, right? This is how it should work theoretically. How do you actually go and do this in real life? So first of all, you have to have qubits that work in such a way that you can harness quantum mechanics. So we build basically artificial atoms. Atoms behave quantum mechanically. We build an artificial atom and we make it out of a superconducting Josephson junction coupled to a microwave resonator. So this is what it actually looks like on the chip. You have these squares that are your qubits and these squiggly lines are your microwave resonators and inside of the qubit is a superconducting Josephson junction. And we gotta cool this thing down to 0.015 Kelvin where zero is absolute zero, right? Room temperature is 300. This is significantly colder than outer space. And we talk to the qubits with microwaves. So this is what it actually looks like. This is how we talk to the qubits. We have inside of a dilution refrigerator which you'll see on the next chart. We have all of these microwave cables that allow you to actually go and probe the qubits with microwaves, okay? And this is what it looks like in the actual lab. So these giant white cylinders, these are our dilution refrigerators. And here you can see my friend Nick working on one of the insides of the quantum computer. So great that we can actually do this but did you know you can play with one for free today? Anytime you want at our booth, when you go home, you can go and you can play with an actual quantum computer. We've hooked up a five qubit quantum computer in the lab through those microwave cables up out of the fridge through the internet available to you at this website for free. So you go to this website. When you do, you're gonna find three different user guides. One is for total beginners. The beginner's guide is no math, just concepts. We're gonna show you visualizations of how the qubits work. We're gonna walk you through early examples of what are the gates you can use? How do they work? How do you think about them? And then you go into the user guide. The user guide is gonna show you some of the linear algebra you need to know. It's gonna show you how algorithms work. We're gonna give you a suggestion of five different algorithms you can run. We're gonna show you what sequence of operations you need to actually implement that algorithm. And then when you're really interested, you're gonna go into the developer guide. This is gonna take you to our API online. It's a sequence of Jupyter notebooks that are written in Python. And you can actually send batch jobs to the quantum computer. Great, just go and learn, right? We're putting it out there for you to learn. This is what it's gonna look like when you click in to the composer tab. On the previous chart, there's three tabs. There's the composer, the user guides, and the community. I'm gonna talk about each of those. This is what it looks like when you click composer. You're gonna get to choose. Do you wanna simulate it? You can choose to simulate where you actually start with two qubits instead of five because it's easier to get started learning with two. Or you can simulate up to 20 qubits. We have a simulator up to 20 qubits. But let's click in to the real device. So clicking in, this is the interface you're gonna see. This is a picture of the actual chip that we have in the lab. You can see those five black squares are our five qubits. And the squiggly lines again are those resonators that we talked to the qubits with those resonators. And here we're giving you some information you may wanna know about the qubits. What's the coherence time? Coherence time is how long does my quantum information last before it gets out of coherence? So for us, we have 50 to 100 microseconds of coherence time. And that matters because it determines how many different operations can I do before I lose my quantum information. Okay, so now let's create superposition. We talked about superposition, let's create it. You drag and drop the different gates that you can perform. There are different logic operations that will be shown on the right. Just drag and drop it onto the graphical user interface. Here I've dragged the H gate into the Hadamard gate onto the score, the line representing the first qubit. And I've done a measurement. And actually these are results I got this morning having run a thousand experiments on the actual quantum computer this morning. And you get roughly 50, 50, zero and one, right? You're in a superposition of zero and one. When you measure it, it randomly chooses. Half the time it chooses zero, half the time it chooses one. If I'm, again, if I'm in a superposition of, if I've got two qubits in superposition then I have four different possible outcomes. Zero, zero, zero, one, one, zero, one, one. So if I measure it a thousand times I get a distribution of each of those possible outcomes. And I can create entanglement. This is how you create entanglement. You put your H gate and then you apply an entanglement gate. This is a C knot. What it says, if I measure the qubit to be zero, don't do anything. If I measure the qubit to be one, flip the second qubit to one. So this is called a bell state and this is an entangled pair of qubits. So you guys can actually go in and create entanglement on a real device in our lab in New York through the internet, which I think is pretty cool. Okay, so now once you get through the beginner's guide and you're really keen, go into the full user guide. This is showing you some of the algorithms that we are enabling you to learn about through the full user guide. If you click into Grover's algorithm you can get a whole derivation of what's going on in that algorithm and we show you this is how you should think about that quantum interference, that amplitude amplification, right? And this is the sequence of gates you need to implement Grover's algorithm on the real device and you can go in and you can actually implement that algorithm on a real quantum computer. So all of this is made possible by a bunch of really committed and passionate makers at IBM. This is a drone's eye view of some of the members of the team outside on the lawn in New York. There's a lot of people that put in a lot of hours to make this available to all of you for free. Okay, and so what are people actually doing with it? We have over 45,000 users worldwide. This is a snapshot of some of them. The white dots represent some of the top universities with the most number of users in the quantum computer. A lot of universities are using it to teach. We have a teacher at EPFL who uses it as part of his quantum information curriculum. We have a teacher at UT Austin. Got teachers all over the world using it. And then we have people just doing really cool and interesting fun things with it. There's a woman, Christine Moran. She's that dot at the South Pole. In between measurements on the South Pole telescope, she's just playing around with a quantum computer, which is really cool. You know, we had a workshop. We telecast a workshop from our Zurich lab to South Africa. So we had three high schools in South Africa just having a quantum information workshop, which is really cool. And this is what those people actually look like. We had Christine in the bottom right at the South Pole. This is the students at the workshop in South Africa. Every summer, starting last summer, so I guess it's only the second year in a row, the University of Waterloo runs a quantum information workshop for their undergraduates. There's a woman, Emily, who, you know, she has a YouTube video that this is I found her on YouTube. She's talking about what it felt like to actually run the algorithms on a real quantum, piece of quantum hardware for the first time. She'd been learning about it in a classroom. She actually got to run it on a real quantum device, which, you know, she talks about being kind of an awesome experience for her. So people are actually using this thing for research. There's been 17, I think at this point, 17 research papers that have been published where someone validates their algorithm on an actual quantum device in a lab. People have made games. There's a guy who created a game called Quantum Battleships, you know? And you actually run the game on the actual processor. So, you know, he's got a blog about it. He walks you through how to actually program the game, how he programmed the game, you know? I just think that's really cool. And people are having fun, right? So the third tab that I mentioned was the community tab. So, you know, in the community, people are asking questions and people in our research team are answering them, right? So what is a CNOT gate? How do I think about that from a mathematical point of view? What is, how come when I have this sequence of gates that doesn't match between experiment and theory? And a lot of the time, you know, because not every gate has perfect accuracy. If you apply too many in a row, then the answer starts to look more randomized because the errors add up. But then, you know, any community is made up of people who like each other and they like to, you know, joke around and have a good time. So that, you know, some of the posts are like, what's your best quantum pun? So, you know, they suggest a couple of drinks like gin entanglement, which I love. And, you know, how do you implement rock, paper, scissors on a quantum computer? You know, we've all played rock, paper, scissors. There was a whole long discussion on one of the community threads. How do you actually implement rock, paper, scissors on a quantum computer? And actually, you know, I'm in the Slack channel where members of our team, you know, answer the questions. And there was like a whole discussion, an actual, like, theoretical discussion about, well, what is the best way to implement rock, paper, scissors? So, you know, it's just fun. You know, people are having a good time. And as of last week, we put a 16-cubit device online for beta users. So this is, you know, this is the most advanced public quantum computer available anywhere and it's free. And if you want to sign up as a beta user, you can just go in and sign up. And, you know, we also had an announcement where we doubled our quantum volume. So not only did we announce 16 qubits, we have another device with double the quantum volume. And there's a picture of Katie in New York in Times Square next to one of our quantum computers. So, I encourage all of you to visit our booth. We have a booth in Building 2. You can come and learn. We have at least two different demos that explain to you how does a qubit work. We have a gyroscope, you know, that's supposed to be able to show you what it, you know, how to think about, you know, mapping a gyroscope onto this thing called a block sphere that you'll learn about in our beginner's guide. And we'll show you a demo of the experience, right? You can go in and you've got that five qubit interface. We've got it up at the booth. You can drag and drop gates. You can create entanglement in a real device online. And we've actually hooked up also some LEDs so that you can actually visualize the results of your experiments. You can run it a bunch of times and see how that quantum randomness, those, that arbitrary measurement of zero and one, if you're in a superposition, how does that actually look when you run it and you show the results with some LEDs? So, I've shown you a bunch of stuff that we've made and what I'd like is for all of you to go off and make something. You know, we've opened up this quantum computer, done it for a few reasons. One of the reasons is we'd love to know what you wanna do with it. What are the things you guys wanna do with it, right? We encourage all of you to go off and make something and share what's your ideas with us. And we'd love to hear about it. So, thank you all for your attention.