We'll talk on, I think, really quantum middleware. It's the term that we're using at IBM. Hopefully you're in the right place. If not, welcome, anyways. Stay. I think we'll have plenty of time for questions at the end. So let's try to get to it. And then we'll answer it. Obviously, if you can't hear me or something like this. OK, let's get started. So Paul is my colleague. He's a senior engineer at IBM. And we're part of the same team where we do open quantum and open serverless. And why those two terms? It seems kind of weird. It's because before this new mission that we have, we used to be part of the team doing Knative, so serverless. And then the quantum team started looking into how to do quantum middleware. And then they started doing quantum serverless. And then, plus, it's always interesting to get new, interesting topics at IBM. So we built this team. And Paul and I are part of that team. And I think you played poker. Hold on. Yeah. And then for me, long-term contributor to open source for us different projects, particularly Cloud Foundry in the past. Even all the way back to Linux kernel and USB. So long history. And you'll often see me with a camera. So if you want a picture, I can promise a really good picture if you want, at least better than average, better than iPhone. Let's say that. So don't feel shy at all. So this is us. So the talk is really three parts. And Paul actually is going to do the heavy lifting, two parts of it. So I'm going to do the first part and try to explain to you quantum computing. And I say try because things are weird when you get to the quantum level. People put Einstein all the time. So let me do it. And even him, if you go back and you look at, he was very skeptical of a lot of things about quantum computing. And it turns out that's how it works. So sorry, Einstein, you were wrong very few times, but he was wrong. Anyhow, we're going to try to explain to you that. Then Paul is going to give you concrete examples, actually, of how, at least at IBM, you right now, obviously not during the talk. But after the talk, you can go and register and start using quantum computer and how to program it. I'll vary the lead that it's using Python. So probably everybody here can program in Python. If not, you can learn it probably during this talk. Super easy language. And you can actually program quantum computers. And we'll show you that. And then the last part is how we are connecting programming these computers with the cloud needs of space, particularly Kubernetes. And you'll see there's a series of use cases that actually it makes a lot of sense to do that, which shouldn't be too surprising to you, but at least we're doing that. And of course, we'll have plenty of time for questions, and hopefully we'll get to that. So OK, so what is it to have a quantum computer? Because this is how it actually looks like, although it's usually encircled because it has to be kept at very, very low temperatures, I think something like a little bit above zero degrees Kelvin. Some of the temperatures, milli-kelvins are colder than, I think, all anywhere in this galaxy, certainly, that we know of. So we're able to keep things at very, very low temperatures. And that's how a lot of what we're going to talk about in the next 10 minutes actually work. So let me try to explain this. So first, before we go is most of you, I'm assuming, obviously, probably are programming computers and understand how classical computers work, or certainly use them. We all have a phone, and it's pretty much a supercomputer. But did you ever think about how it actually works in terms of the hardware, like the details? It's surprising that not a lot of people notice because we sort of assume computers pretty much everywhere, so it's kind of assumed. But the hardware is very interesting, right? So it works in a very bastardized simplification is that you hear all the time, and it's exactly that true. It's zeroes and ones. Why? Because we can easily represent those two signals in terms of the electronics. And of course, zero, you can think of ground, and then one, some kind of voltage. And of course, you start thinking, well, how does it actually work? The computer is really just making simple computation, but it does a lot of them. So then you start thinking, OK, so how does it actually work?  How does it actually work? And then they say, no, no, really in the details. And you can actually explain this. And the way I try to explain this to people is to think of, how would you do a simple addition, like two numbers? And first, how do you represent two numbers? Like a simple digit. And you can start thinking. And you start looking at the fact that we're using numbers. You could use binary numbers. You explain that. And I think everybody here would understand. Then binary numbers is zeroes and ones. OK, cool. So we can represent them with zeroes and voltage. So one. And then you start thinking, OK, how do you add? Well, it's not too hard to build a Boolean circuit. And you explain that to people how to actually do additions with Boolean circuits. And it turns out that NAND gates pretty much is all you need. You can reduce everything to NAND gates. And the first class of computer hardware, they basically show you how to build adders and all kinds of simple circuits. And it turns out that from these very, very simple circuits, you can build pretty much anything. Why? Because you can come up with what you say in instruction sets and then very simple operations that are all taking zeroes and ones. And then you build essentially a processor. And you're living a lot on the table. But the basic idea is that you start with circuits. And you have basic inputs that are zeroes and ones. And from that, we can build what we have today. There is one important thing also to mention, besides the fact that it evolved to a fact that I think the latest processor that you have on your Apple phone has billions of circuits, billions of transistors, is that all of that works perfectly, meaning that you put zeroes and ones, things don't change. They stay exactly that. And maybe there could be errors in the circuit, but it gets corrected. You add additional redundancy. But there's not any errors. Like, can you get a new Intel chip? That chip, if it has errors, it's a problem. You just return it and they give you a new one. So the things work very, very precisely. So it's very deterministic. And that's also another important thing, because quantum computers are not like that. So what is a quantum that moved to the next slide? So in many ways, and I'm going to explain the quantum computing piece later, but once we were able to build those classical computing, if you may, as you know, we solve the world runs on computing. That's why we're here. I think Jim had a talk, I think it was in November in Lake Tahoe, where he said Linux was responsible for some ungodly number. I think the world economy is like $100 trillion, and Linux impacted a third of that. And so it's all computing, and it's on, right? So there's a huge number of problems that you can solve. But it's interesting to also mention that there are problems you cannot solve. And it's usually around optimization, at least the problems that we care about. But there is a huge class of problems. And of course, if you take computer science class and complexity, then you start learning that not only are there problems, we cannot solve the current model. It's essentially they haven't proven it yet, but it's almost impossible to solve those problems. And increasingly, those problems are very, very important, or at least interesting for us to solve. And people have spent countless PhD thesis trying to prove this. But usually what you end up doing is you come up with heuristics to solve them. So you still solve them, but use heuristics. And part of the problem is that because the way classical computers work is the same, right? So whether it's your iPhone or it's a mainframe, it's very much based on a Turing model. So they work very much the same way. And the thing about quantum computing that makes them exciting, although I haven't even explained to you or attempted to explain to you how they work, is that they can actually solve problems that are outside of this space. Because they really work in a very different fashion. And we're going to try to explore that. And because of that, it makes them not only interesting for research, but also for practical applications. And there's actually one specific practical application of quantum computing that is sort of in the horizon that's going to impact all of us. And we're going to talk about that specifically as well. So some examples, if you're Elon, and you're trying to build the next better EV with more range, you're learning, you probably have tons of people figuring out the chemistry of batteries or longer lasting batteries. And I know that people at IBM, for instance, it spent a lot of time looking at lithium iron and then lithium air. So instead of using iron, use essentially what's in the air. So there's tons of different sort of chemical problems for material science that you need to, people are exploring because it has practical applications. And the problem is that even though we understand the chemistry and the physics, the equations end up being very difficult to solve, or at least difficult to simulate, because there are so many interactions between the different particles and your molecules. So these kind of problems where the number of input is very, very large tend to be overwhelming for even supercomputers. And Paul at some point is going to give you a specific example of chemistry problems that we're solving with quantum computing. And tons of companies that we work with are actually doing this in universities as well. So that's one problem. Another problem that people know very well, because it's sort of well studied, is the traveling salesman. Essentially, how to optimize your path to, let's say, Vancouver, maybe hitting all the different coffee shops. How to optimize that. It turns out to be very hard. It's like, for instance, if we said, I don't know how many people we have in the womb, I'm assuming about 15. And there is maybe 50 chairs. And if we said, OK, let's rearrange those chairs. Even if we had just 15 chairs, and I sat one place, and I said, let's rearrange. With 15, it's 15 factorial different ways. And 15 factorial is a very large number. You can check it. Try to compute it. You'll see. So these kind of problems are sort of optimization problem. They are very hard to solve in classical computers. Why? Because the number of operations is large. So even though, with very basic circuits, we can build computers. And with 5 billion transistors, we can build computers that do a lot of computation. The numbers get so big that it's even hard to comprehend how big a processor would need to be for a certain input. So in other words, as the input goes, if the number of operation is exponential, then you reach the limits of classical computing very fast. So what you have to do is you have to use heuristics. But some problems you can't. Or we don't know any heuristics. So that's the intuition I want you to remember. Because we're not going to tell you that quantum computers are going to be in your pocket and you're going to be running your TikTok or your Instagram on them. No, but for some class of problems that normal computers cannot solve, they are actually surprisingly fast at solving those. So that's the key. So how does it work?  So one of those problems is this problem. What? What do we look at like that? It's essentially the way encryption works today. And we mentioned this problem. We spent a lot of time on it. Not because it's mainly because it impacts everybody. And there is actual solution, an algorithm right now that exists and it existed since 1995, to solve this issue on a quantum computer. So what is that? So again, bastardizing cryptology.  If you're not, you should. You heard the talk by Eric this morning. You should be. And hopefully you are already. OK, so how does that work? That actually works very simply by the problem of factorization. So of course, there's tons of different kinds of encryption. But the basic encryption that most people use right now is based on sort of RSA. So it's like a symmetric encryption. So the gist of it is this. Numbers turn out to be easy to multiply. You could multiply any number, how large they are. Most of your iPhone can do this. You can just use it on your calculator on your iPhone. Just put two large numbers, multiply them. But now if I give you the multiplied, the result, and I told you, without telling you what those two initial numbers were, and I asked you, could you come up with two numbers that, when multiplied, give you the original, the result? Turns out to be a very hard problem. And it's a problem we've known for a long time in mathematics. Why that is, is maybe outside of the realm. It's probably in number theories. But just understand that it's hard to, if I give you a number large enough, and I told you, come up with the initial two, let's say, prime factors. You could spend the whole week using the current computers you have, the best algorithms, and you would not have a solution. And that's how encryption work is, because you can use these kind of two values. That's why you hear people say, you have a public key, a private key, and you can share the public key, because once you have one of them, then if I encrypt with the result, then I can decrypt because I have the private key. But if somebody has to guess the number, the resulted number, or two of those values, you can't. So because of that, you can assume that as the more you use large numbers to do these kind of encryption, the harder it is, because it's exponential. So there's a chart later on that shows you the actual number of computation that you need to do for some digits of encryption. But why is this particular problem important? Well, it's because there is a solution to this problem with quantum computing that can be done in hours versus years or thousands of years with the largest computer we know right now. We're going to talk about it a little bit more. Let's move to the next slide. So now finally, quantum computing. So before giving you a little bit more detail about the impact of these computers, let's think about why quantum computing is different and what's so special about it. So the best way I found, and I'm still trying to find the best way, and I stole this one from, I think, somebody online I saw explain this. So I have here a coin. It's a Canadian coin. The US, we practically don't use coins anymore. But it's a nice coin. It has the face of the queen. So we could assume this has to be one, and then heads. And then the other side is tail. So you could assume it as being zero. Quantum computers work on what we call qubit. So to understand what qubit is, that's why I'm using this coin. Whereas a bit, I mentioned, is either zero or one. Cannot be both. It's very precise. The whole computer architecture is designed to essentially assume that those bits don't just flip. Quantum computing are more like this, where you have zero and one, and then you flip it, it's going to catch it. And it's tail. So zero. And you keep doing this, and you essentially have an idea of what qubits are. So they are actually in both zeros and one at the same time. And of course, you resolve them by observing it, and then it can be zero or one. So anybody here has a coin? Well, if you did, you could do the same thing as me. So if we did it at the same time, then we would explore it. Chances are we're not sure. Yours could be zero, mine could be one. But quantum computing uses something else in addition to what we call superposition, the fact that qubits can be in both zeros and one at the same time. It uses something entanglement, called entanglement. And it's another one of those weird realities of the quantum world of nature. That if you had a coin like me, so hopefully any coin, I guess, it doesn't have to be just a Canadian coin, and we were entangled, and we flipped it, and we observed it. My observation is head. If those two coins were entangled, I would guarantee you that your observation will be head as well. And that's what's interesting. So first, that the coins are at both, or the qubits are in both states, so superposition. And then when you entangle those qubits, that you can observe the same result or the same value. And it's hard to put your head around it, but people have done multiple experiments to show how entanglement works, and it works. I think the Chinese did, and I mentioned the Chinese because there's another reason there. There's a huge group of Chinese researchers doing quantum computing and, of course, quantum physics and so on. And I remember maybe a few years back where they sent one of their walkets. And as part of usually we send walkets, you do experiments. One of the experiments was to show entangled qubits and show that they actually, even long distances, they will read the same. So they still entangled. So distance doesn't matter. It's a problem, right? That's what Einstein actually had problems with, because if you had one, and I had one, and we flip it, and it's entangled, how could it be the same? Is there communication between the two? And if there's communication, is it faster than light? All kinds of crazy stuff. So anyway, those two aspects, superposition and entanglement, means that when you build, all of a sudden, a circuit, a quantum circuit, you can actually have solutions for all of the different inputs.  And this is where the entanglement part comes in, when you can start entangling things. Then you can actually get results that you want. Now of course, not all problems fit this model very well, but there is actually one problem. So let's go to the next slide. And it's Peter Schor's algorithm. And just an example, there are tons now of problems that are interesting to us that you can solve with quantum computing. But this one impacts all of us. And this graph is just showing you the number of the complexity. So currently, when you use your phone and you're using WhatsApp, you are encrypting, I think it's like 256, so somewhere here. So some digit that is very large. And for the best known algorithm to do factorization, number sieve, the number of operations that you need to do to compute, it's very simple operation. Divisions, multiplication, remainder, that's it. But you have a lot of them to do for large numbers. And right now, the estimation is about 10 to 25, 10 to 30. And you think, OK, well, 10 to 30, what number is that? It's easy to write. But it turns out to be more stars than the known universe. That's what 10 to 30 is, which is like, OK, yeah, serious. And then if you keep increasing to a point where it becomes 10 to 80, so let's say 1,000, it's more not stars, more atoms in the whole universe. That's what bigger number. So in other words, the number of operations that you need to do is impossible. So that means that unless we come up with better algorithm, your encryption will always be perfect. And remember, it's hard to figure out the prime numbers. But if you had two numbers that were 1,000 long, computers these days do this in milliseconds. So one way is easy, the other way is hard. So that's the key. Sure, figured out an algorithm in 1995, as part of his postdoc at Berkeley, that actually solved the same problem, assuming you have a large enough quantum computer at constant time. So you can see for 300, it's 10 to the 5 operation, which is pretty easy. Most of you might have more than that in your bank account, I hope. So that's the key, OK? So how did that happen? And it's complicated, but the gist of it is he came up with a way to do Fourier transforms with quantum computing. And Fourier transforms are great for all kinds of different signals. But it turns out, and this is the intuition, it turns out that when you are doing very basic factorization trying to figure out the factors of numbers, you will see repeating stuff. So every time you see repeating values, then you should think of sine waves or cosine wave. And you should think of Fourier transforms, because that allows you to extract signal from noise. And then that's how actually Schor is able to do it. So last year in November, back to our friends in China, there is a paper they would saying that they were able to crack WhatsApp messages in less than a couple hours. And it turned out that they made some mistakes, but the point is it's getting very quick. And they were running it on their quantum computing. So at IBM, our best quantum computer right now was, I think, announced yesterday, Jay mentioned something on LinkedIn, where it was 433 qubits. But very soon, when you start getting into the 1,000, to maybe like a few 1,000 qubits, then practically every encryption will be decrypted. And things move very fast also. And right now, the projection is probably around 2030, at least for us. That's what we think. Other people might get faster or longer. But this is one of those problems that once we reach that, there is no secret anymore. So we have to approach different ways. So there are different solutions for this. And we could talk about this in the Q&A. But let's move now to the second part and third part of the talk, which is going to show you concrete ways of solving or using quantum computing. So hopefully, I wet your appetite. If not, Paul is going to go into very concrete examples. Thanks. All right. So how do we do quantum computing? So to kind of lean back on the history piece, if you remember back, classical computers, initially, we built. There were electrical circuits that we wired. How do we do similar things with quantum computers? It still involves kind of building circuits, which isn't easy. So we've built, basically, an SDK called Kiskit that enables one to use Python to build these kinds of circuits. If I just jump back to this slide here, like the circuit here that we have up here, one would have to specify out kind of all the different pieces of that. We've got the Kiskit SDK, which allows one to build that, which is a couple of kinds of Python code. So just to kind of talk through Kiskit really quickly, it's going to skip this pretty quickly. But it's an SDK for building quantum circuits and quantum tools. So there's algorithms that are implemented in Kiskit. There's simulator. So if you don't actually have access to a quantum computer, you can still run quantum computer or quantum computations. It's helpful for things like CI, where you don't necessarily need to call out to a production system. And the basic way this works is, again, it's similar to kind of when we had classical computers, we started with assembly. We wrote higher level languages like CI and things like that that enabled us to write programs that run on those that made it easier. How does any type of thing like that work? Four steps.  That's step one. Then you compile it. You translate it into a set of instructions that will run on the machine that you have. In this case, it would be a classical computer. Then the fun part, you run it. And then you look at the results of your program, analyze it, and go forward from there. So that's basically kind of how Qiskit works, how the quantum workflow works. So to demo this, let's take Max's example of flipping a coin. Let's say, for some reason, we want to build a quantum coin flipper. It's going to cost you a little bit more than $0.25 Canadian. But let's see what that might look like. So this is a Jupyter notebook that's going to run the Qiskit tooling to kind of show us how to work through this. So let's start step one. The first thing you want to do, again, this is all Python, so it should hopefully be pretty self-explanatory. But the first thing we're going to do is we're going to do this in kind of a quantum way. The first thing we want is we want to build a circuit. And we just declare a quantum circuit. We're going to use two qubits, so two quantum bits, and then we're going to have an output of two classical bits at the end. So we shall run this. The next piece we want is we actually build the gate. So basically what we're going to do is enable us to get a coin you flip it as long as it's not a two-headed coin or a fake coin or a cheating coin. You get a 50-50 distribution of heads and tails in the long run. So we want to basically do something like that with quantum. So we can basically set up a quantum gate that will give us a probability distribution of 50-50 in our outputs. We do that by adding an H gate and a C not gate, and then measuring it. And again, it's fairly simple. Circuit.h adds an H gate on the zero qubit. We put a C not gate or a CX gate by CX. And then we can look at what that looks like. The nice thing about these things is we can build our gate. And there's our little random gate. Those are things we want to do. So then we've built our gate. Now we've got to compile it. So I'm going to use a simulator because I don't trust conference network, no matter how much assurance we get in keynotes, that it's going to be great. So we're going to use a simulator here to build this thing. We just declare a simulator. And then we'll use a transpiled method to compile it. So that's basically just going to compile it down to this simulator set of instructions on the back end. And then what we're going to do is now we've got it. So now we're going to run it. And again, as I said, quantum computations, they're probability distributions. It's going to be basically hoping we're going to get a 50-50 distribution of outputs. We're going to run it 1,000 times. And then take a look at the outputs. So let's do that. And let's just take a count. So what do we get? 5, 10 versus 490. So our 0, 0s versus our 1, 1s, that's 50-50. And then, of course, we can do a nice thing and plot some histograms that may or may not actually fit on the screen, but that's 50-50 distribution. So that is a quantum coin flip with a couple lines of Python. Kind of some neat tooling that we can do with Kiskit to do that. All right. So that's Kiskit. It's open source. It's on github, github.com, slash Kiskit. We've got links at the end where you can get access to that as well. So that's how we do it. How do we do it in a cloud-native way, particularly how do we use Kubernetes to kind of interact with quantum computers? And one way to kind of think about this is that Kubernetes is essentially an orchestrator. It allows us to run different tasks. And we can call it a different workload. So we can use Kubernetes to run workloads on cloud. I think everybody's familiar with that. We can use Kubernetes to run workloads on prem. And our data centers and that type of thing. We can use Kubernetes to run workloads on high-performance computers. And kind of the takeaway from kind of all these examples is that Kubernetes essentially allows us to run different types of heterogeneous compute. Quantum's a type of heterogeneous compute. We can kind of add that into the mix. And so it just becomes another type of compute resource that we can use in the type of jobs that we're building and running via Kubernetes. So to something a little bit more complex and flipping a coin, a typical workload that we're running, a computation or something along those lines, we can start off with a big problem. But we can break that problem into a series of different tasks. And each of those tasks may have components and things like that. And each piece of that puzzle, there's going to be a particular way that we can optimize it to run it in the quickest amount of time. Some of those tasks might run best on HPC. Some of them might be quantum tasks, or if we want to factor algorithms, like that's a quantum task. If we want to do some Monte Carlo simulation, there's quantum algorithms that are much quicker than some classical ones. So depending on what the type of task is, we want to break this big problem down into smaller problems so we can solve them, recombine the results, and get our output. It's classic dividing conquer, like you might have done a merge or something like that, break big problems into smaller problems so you can solve them faster. So that's the tooling that we've developed, something called quantum middleware. How do we do this? It's taking that problem and using Kubernetes to orchestrate these different pieces. So in a quantum case, we've identified a workload that should be run as a quantum workload. We can ship that job out to Kubernetes. We can let Kubernetes handle the scheduling to the quantum back ends, bring the results back, and continue forward. That's abstract. Let's go through a couple of examples. I think we've got about five or 10 minutes left. So I'm going to do a chemistry example, and then another one if we have time. The chemistry example, so let's, assuming we're a chemist, we're modeling batteries, something along those lines, we want to optimize a chemical equation. We want to optimize a molecule, which we're going to simulate here by calculating the ground state of a lithium hydride molecule. So the way we do that is we have an equation that we can use to solve that. We have parameters that we want to tweak to figure out what the best version of that is. With quantum, that involves you build a circuit. You set the parameters to it. You send it off to quantum to run, and you get your results back. Lithium hydride is kind of trivial if you're building something bigger. That can take time. So how do you kind of parallelize that so you can speed it up? Kind of bury the lead there, but you parallelize it. So we can use kind of, Kubernetes is kind of that parallelizer. We can take these different quantum tasks, and we can ship them out, divide and conquer, get the results, pull them back, recombine them, and return them using Kubernetes to schedule that. So let's take a look at what that might look like kind of very quickly. So there's, I guess to back up a second, there's kind of two ways we can run these quantum workloads in Kubernetes. One is we can build a self-contained program that we can send off to Kubernetes and allow that to kind of do the scheduling there. We can also run some of these things locally and then schedule specific tasks that we can farm out. So the first example I'm gonna show you here is kind of the, you know, off-put everything, or we define what we call a quantum program, which we've got, what, two minutes, so this is gonna be quick. So basically what we're gonna do is we're gonna define a program here. Basically what happens here is we've written a Python script that has the quantum calculation we wanna do. We're using a VQE, which is a quantum algorithm to solve the ground state energy problem. And we wanna run it with a couple of different bond lengths. Basically what we're doing is we're seeing how close the molecules should be to get a specific energy result, which I'm not a chemist, but matters a lot. So basically what we're doing is we're gonna run which I'm not a chemist, but matters in chemistry. So we're just gonna run this in a loop, we're gonna fire each of those off as independent tasks, and what's gonna happen is we're gonna take that Python script, farm it out to Kubernetes, it's gonna do the run, it's gonna make the quantum call, get the results, pull them back, and ship them off to us, and then we're gonna run that for a couple of different bond lengths. So what happens is we're using kind of an actor model here, but we've defined basically four jobs, each of those jobs get run, we can watch the results, and this is where things get fun. They all succeeded again, this is a trivial calculation so it doesn't take super long, but we can go through and take a look and see that for each of those different reactions, so for example, for bond length 2.55, we get a total ground state energy of negative 7.5 for 3.0, we get negative 5.8, and so on and so forth. So depending on kind of which, what matters for our output, we've run this multiple times, speeding up the calculations, assuming these things took longer than we had for the demo here. All right, demo three, are we at, how much time do we have left? Okay, do it anyways, all right, cool, circumnaiting. So Max said that our quantum computers, again there's a number of qubits, so we're like 433. Sometimes you might need more than that, which would basically mean if you needed like 5,000 qubits, you're kind of out of luck for the time being until the technology gets there. But what if we could take that big circuit and divide it into pieces that were equivalent to the larger problems? That's one of the things called circumnaiting where we can basically decompose a big problem into a set of smaller problems that are functionally equivalent to the big problem, solve those smaller problems easily and then return a result. And we can, so again this is, again we can do this in Kubernetes where we can decompose, ship the results out to quantum, pull the results back, recompose, and return a result back to a user. Which, we're gonna use a bit of a trivial example here. This is an eight qubit gate that we're gonna build that lets us assume that we could not, we don't have an eight, for some reason we can't, we don't have eight qubits, we can't solve that. So what we can do is we can use circumnaiting. So here where we're gonna use the second example, I'm just gonna run everything in my notebook but I'm gonna ship out the computationally expensive parts to Kubernetes to handle for me. So I'm going to define a cut function.  And then what's gonna happen, so then I'm gonna call with serverless context which is gonna run it via Kubernetes and this is gonna take its time but it's gonna basically go through and look and figure out where can I cut this big circuit to get a functionally or two functionally equivalent smaller ones. And so it's done all this stuff to get that and then if we take a look, we can see that now we've got one circuit that's got five qubits and a second circuit that's got five qubits as well. So we've went from eight qubits to five qubits. Again, a trivial example but it's indicative of kind of the larger pattern. And then what we can do is basically run, we can run these smaller things get a result and then we will have a solution to the bigger problem using smaller problems, divide and conquer. So given that we're running low on time, so what can we kind of take in terms of conclusion out of this? I think there's three kind of main take away points to mention here. One, some of those hard problems that we aren't able to solve with classical computers, quantum gives us solutions to them which can be good or bad in terms of encryption may not be the best thing in the world but they're optimization problems, they're hard problems we can now solve with quantum computers. And not just quantum, if we combine quantum and classical together, we're able to solve these bigger problems in a more efficient way. It's kind of the marrying of the two technologies that make things better for us. And last but not least it, Kubernetes really kind of serves as the natural orchestrator in terms of how we run these workloads and orchestrate them. So there's links here. I think these slides will be online too if you don't want to copy QR codes because you never know. I mean, they're good QR cards as far as I know but I heard there are vulnerabilities in QR codes as well so caveat up there, you know. But the links will be online. Yeah, thanks everybody. I guess if we have questions, I guess. I have one question, I have a burning question. Hi, you had mentioned IBM gave 2030 as a, around that time. And was that without factoring in circuit knitting as an approach to breaking down shores algorithm? So what would you say a modified timeline would be if somebody had a lot of circuit knitting experience? So one of the things that circuit knitting is it requires actually being able to decompose the larger problem into a smaller problem. It's not a magical, like you can't just, you know, you can't necessarily subdivide everything and make smaller problems out of it. So there's, I mean, if I had a, I mean, there's a lot of ifs in there and the best estimate that we have, I think is 2030. I don't, I mean, you know, there's any, you know, whole number of things could happen in there but I don't think we want to get into speculating in terms of what could happen or what could not happen. The other thing I'd mention is that I personally wouldn't even wait for even 2026, 2027 is to start exploring better cryptographic algorithm. So to solve, to get us into that, you know, sort of post-quantum world that we call, US government actually in January 9th, Biden signed a law that requires all US entities and I'm sure in Canada they probably have some similar law, I'm not 100% sure but they probably will follow to move us to post-quantum and the reason we can do this is because as we discussed the problem of cryptography, at least, you know, I say type of cryptography is based on factorization. So then they ran for the past 10 years, people have been studying what could you do beyond that, right? And there's ways to essentially encrypt data using completely new math. So it's not based on, you know, the basic number theory issues of factorization but it's based on lattice and lattice math is complicated but the basic thing you can think of is, you know, if you think of a lattice as a space, multi-dimensional space, and you have vectors on a space, it's finding how to combine those vectors to reach two points. And it turns out to be extremely hard because there are so many different possibilities whereas if I give you those two vectors, it's easy to find a combination to get from one point to another. If you're interested, we can talk about this after but the basic point is try to start learning about what we call post-quantum cryptography. And I'll do a free plug. Paul and I are part also of IBM's post-quantum efforts and you know, there is a open quantum safe. It's called open quantum safe. If you look into that.org, you'll find for instance, modified version of OpenSSL, Curl, HAPoxy, we have team members contributing to all of that that are essentially moved to using these new cryptography that use lattice.  So easy to remember. So if you're interested, you should look into that or come talk to us or go to the ibm.com slash quantum and you'll find a quantum safe page that has tons of information. So let me stop there because the order talk is gonna start but we'll be outside. And also I think you're here until Friday as well as me. If you find us, we are always happy to talk to you. Thank you.


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If I was to ask you, what is the leading cause of neurological disability worldwide? What would your answer be? Stroke? Dementia? Well, what if I was to tell you that the correct answer is migraine? Because it is. Migraine is a third most prevalent disease on the planet, affecting over 1 billion people, including me, and including many of you here in the audience tonight. And as all of us who have ever felt or looked something like this will agree, it's much more than just a headache. Migraine attacks cause severe pain, gastric disturbances, light sensitivity, and inflammation. And in chronic cases, migraine attacks can occur over 15 times per month, leading to a significant level of disability. Unfortunately, we aren't able to cure migraine, but drugs do exist to manage the symptoms when a migraine attack occurs. However, these drugs don't necessarily work in every patient, and even if they do, they come with a wide range of side effects, some of which may be just as de-abilitating as the migraine itself. As such, new drugs need to be developed. The goal of my research is to pave the way for new migraine therapeutics. We know of a protein in the brain called pituitary delite cyclase-activating peptide, which makes my head hurt to say, so I'll just call it PACAP. PACAP binds to the PAC-1 receptor in the brain, leading to the onset of a migraine. Thus, it is thought that if we can stop this interaction from occurring via a drug, this may prove to be a new way of treating migraine. As this interaction isn't fully understood, I aim to uncover the way in which PACAP binds to the PAC-1 receptor so as to aid future drug development efforts. The binding between PACAP and PAC-1 is like fitting two puzzle pieces together. When they bind, the PAC-1 receptor is activated and sends a signal leading to the onset of a migraine. Now, I can take part of the PAC-1 receptor and mutate it, and if that part of the receptor is important for binding, mutating it will effectively change the shape of that puzzle piece, meaning PACAP can't bind as well. This results in a change in the signal that the PAC-1 receptor produces, which I can measure. Alternatively, if that part of the receptor is unimportant for binding, mutating it won't have any effect on the way in which PACAP and PAC-1 bind. If I mutate different sites on the receptor one by one, I can see if the signal produced by the PAC-1 receptor changes, and this allows me to build a picture of the exact way in which PACAP and PAC-1 bind. With my research, future studies can create a drug which binds to the PAC-1 receptor in the same way as PACAP, but instead of activating it, blocks it, telling that migraine to PAC its PACs. Thank you.


Hello everybody! In today's video, I'll show you how to fix a problem with the Internet connection, namely, DNS server is not responding. DNS server issues are among the most widespread errors related to Internet connections. Once such error appears, there is no Internet connection, the icon in the system tray becomes like that, and if you open a browser to visit a website, you will see messages like server DNS address could not be found, error name not resolved, and so on. This problem is caused by a failure of the DNS server which is in charge of redirecting the IP address. In today's video, we will go into what could trigger such an error and how to fix it. Hello friends! If you need to recover deleted data, view or restore remote browsing history, Hetman software products will help you. Follow the link in the description, download the necessary program for free, install it and analyze the disk. The utility will show you the data you can recover, so you will be able to view it or get it back. In our channel and blog, you will find solutions to any problem, from installing an operating system or configuring it to fixing possible bugs and errors or optimizing mobile gadgets. Our specialists will answer any questions you ask in your comments under the videos or articles. The problem can be caused by your computer, DNS server not running, ROM network settings and so on, or by the router, malfunction or ROM configuration. The provider's equipment can also cause the trouble. Most modems are configured to receive DNS settings automatically from the provider's servers. Sometimes, provider's servers on modems may work too slowly or incorrectly, which results in various internet connection issues. The first thing to do before you start troubleshooting is to restore the computer and the router. With the router, this can be done by unplugging it from the power outlet for about 20 seconds, or by pressing the power button, if your device has it, of course. When both devices are stunned, begin the network diagnostics. You can do it by right-clicking on the network icon in the system tray and selecting the line troubleshoot problems. After diagnostics, the network will either start working again or you will see the error of the DNS server isn't responding. The next step to take is to check if the internet connection works correctly with another device. If your computer receives the connection through the router, check if other recipients working with this router have any network issues or DNS server problems. For example, connect your phone to this network to see if connection can be established and if there are any errors involving DNS server. If the problem is caused by something in your computer, other devices will work properly with the internet. In case your computer is connected through the router, try establishing an internet connection directly without the router. Put the network cable from the router and stick it into the network port of your computer instead. Also, it would be useful to remember where exactly the DNS error appeared and what you were doing before internet connection problems began. You may have changed certain settings or installed some software. Before you configure the settings, check if the DNS client service is running. Open the services window. Press the key shortcut Windows plus R and run the command services.msc or look for services in the search field. In the window that opens, find DNS client service, right-click on it and select properties. The setup type should be automatic. If the start button is active, click on it. Then click Apply and again. If the service was stopped and you started it, the internet connection should be restored as soon as you restart the computer. If your situation is different, try another solution. The next step is checking DNS server configuration in your network and internet settings. If there are some addresses in the corresponding fields, you can configure the settings to receive addresses automatically or use DNS addresses by Google. This method often helps us to get rid of the error DNS server is not responding. Open settings, network and internet, network and sharing center, change adapter settings. Right-click on your internet connection icon and select properties. If your computer receives internet by cable, it should be other net. And if you are on Wi-Fi, it's a wireless network. In the window that opens, select IP version 4 and click properties. If you have a certain DNS server assigned, check the option for Obtain DNS server address automatically and click OK. Restart the computer and check your internet connection. If you have configured this connection to obtain the address automatically, but there is no internet, using Google addresses may help. Check the option for Use the following DNS server addresses and type the preferred DNS server address 8888. Select the alternate DNS server address 88844. Click OK and restart the computer. In most of the cases, this should fix your problem. If you manage to solve the problem on your computer but other devices are still in trouble and they are connected to the same router, these addresses should be entered into the router as well. In case you are interested, visit our YouTube channel for a good video about configuring a router and I will link the link below. Open the settings, network, WAN or internet. Change DNS here and save the settings. If it doesn't help to fix the problem, try cleaning DNS cache and other network settings. Watch the command prompt as administrator and run the following commands one after another to clean the cache and network settings. Run the following commands one by one. In Windows 10, you can do it by resetting the network. Open the router settings, network and internet, status and find network reset below. Click to reset it now. After that, restart the computer and check the internet connection again. One more close behind the internet connection problem could be missing network adapter drivers. Although such error is quite rare in this case, it's better to check all possible causes to be on the safe side. Well, open the device manager by right-clicking on the start menu. View the list to see if there are any devices with a yellow exclamatory mark next to them. If there are some, it means they need to have a driver installed. You can learn more about downloading and installing a proper driver by watching one of our videos. Follow the link in the description. Right-click on the device, update driver. One more thing to trigger this error is installation of an antivirus or configuration of the firewall. Also, this is what happens when you set your antivirus to maximum security. Try to disable the antivirus for a while. If the internet connection is restored, then is the antivirus to blame. For example, a vast antivirus often interferes with Windows network settings, thus causing various issues with internet connections. For example, your internet connection is down when you uninstall the antivirus, or a DNS error appears or the network adapter suddenly encounters problems with IP configuration. In this case, what you need is to stop the antivirus. If it doesn't solve the problem, uninstall it. An alternative is to reinstall it, but without any extra components. Finally, the last step to take is to contact your provider about this issue. If you tried all the methods in this video, but the error is still there, contact your internet service provider's tech, support, and explain your problem in detail. Quite often, DNS issues can be caused by things that only your provider can fix. If your provider insists they have no problems at all, the only way out is to reset your operating system. And there is a special video about it in our channel – check the link below. And that is all for now. Hopefully, this video was useful. Remember to click the Like button and subscribe to our channel. Hit the bell button to receive notifications and never miss new videos. Leave comments to ask questions. Thank you for watching. Good luck.


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Hi everyone, Joe for jaspyscasebrakes.com, coming at you with 2022 Bowman Chrome Baseball Hobby Edition, 12 box pick your team number of 15. Now Bowman Chrome fans, go to jaspyscasebrakes.com right now because we've got more hobby in the store and HTA back on the menu. So check that out, jaspyscasebrakes.com, all card ship, big thanks everybody here for making it happen. Most of the teams were kind of randomized and that was in that team random. So there's sort of a hybrid random and pick your team kind of action here. Jacob, you got the pirates last before we pulled them for the filler. So that's last spot mojo for you 70% of time. Last spot mojo hits 100% of the time. All right, here's the big case and my phone is in the way. Got the football game on right here. Let's pop this case open and see what we got. I think we have more HTA and hobby on personal breaks as well. At jaspysbrakes on Instagram. It's speaking of you take this out of my inventory system here. I forgot to do that before the break. It's going to take just a couple seconds here. It's a 12 box case right now. All right, here's the first box. Good luck and quiet on the baseball front, right? Not too much going on in the baseball world. Some small little deals being done here and there, but no major no major trades yet, no major free agents locked in. But the the winter meetings are coming up. I think on, I think they go from Sunday to Wednesday or something like that. They're going to be in San Diego. And a really nice hotel that I've stayed at before. So really nice spot downtown San Diego. They're going to get some discussions going. I think there was some rumor that or some speculation, a little smoke that that Aaron Judge kind of wants to get the deal a deal done sooner rather than later, maybe at the at the winter meetings. Maybe his mother free agents will get signed there during that weekend as well. Also, is it just me or are the uniforms very similar for the for Buffalo and the Patriots for Buffalo and New England? Like the helmets look very similar. The white helmet and the blue and red stripe in the middle. A little confusing at first glance. All right, first box. Good luck. All card ship is Christian Hernandez. One seventy seven out of two fifty owned back here. Penny Sleuth is right here. Edgar would like to have belly back, but at a what? But at a low, low one or two year deal. Yeah, I think I mean, I think just his defense alone is worth getting him back. But right, it's got to be at a really low level, but I don't know. There's Christian Vicaro for the Nats, Sean Maddock and Jason Curio for Cleveland. That's for Jacob. We're also looking for his brother Jackson for the brew crew. It's Bobby Whit Jr. I don't know. What would you pay him, Edgar? What's low for you? And there's Yerlin Confidan for the Reds. There's James Altman. He might get some shots of the Dodgers next year to one fifty. Might be maybe a replacement for Bellinger. There's a Kubrick Hayes. Refractor four twelve out of four ninety nine going to Jacob and the Pirates. And John Rhodes one seventy three out of one fifty. I'll go to Mark and the Orioles one that in the filler. Now, I wouldn't mind giving Bellinger like a a one year deal for. I was going to say the same thing. Edgar ten million a year. Right. So it's much, much lower than the qualifying offer of almost twenty million dollars, like nineteen and a half million dollars or something like that. There's much lower than that. I think I think that's worth it. And obviously if he has a bounce back season offensively. You know, I mean that would be that would be a bonus. I think the Dodgers have invested so much into him over the years. You know, it's more likely that spending Montgomery for the Rockies. By the way, Matthew, it's more likely that the team that drafted a player will most likely give him another chance. I mean, how many other teams are going to get Bellinger for for more than that, you know, to take a gamble on him. Maybe the Giants. Just because just because Farhan up in a Zaidi up in San Francisco already has some sort of familiarity with him. But other than that, hard to see a lot of other teams really taking a flyer on him. Unless it's unless it's like they've they've exhausted all other options.  We try to try signing all these other guys. But yeah. I don't I don't think unless Bellinger falls into their laps at a really team friendly deal. I don't think they're going to resign him. Yeah. What do you do about Justin Turner? I feel like it's either going to be Bellinger or Justin Turner as the as the guy that they bring. I think it's going to be more JT over Bellinger. I think they'll sign him for another year or two. But you don't want JT back. That's a tough one, too. But he's kind of he's got to come back. Knowing that he's going to be in a in a off the bench role, not not a starting role. I think that has to be made clear to bring guys like Bobby Miller up for the Dodgers. There's your Danny De Los Santos 23 out of 1 at 99. Magenta Schimmer. Exactly. Yeah. If he takes that Chase Utley kind of role, and if he knows that going into it, you know, then I'd be like, hey, welcome back. Small little deal. There's a Samuel Munoz for the Dodgers. That's going to go to Edward. But I agree. Edgar, I do think that there needs to be a little bit of a shakeup in the locker room. It just needs to be a little more. A little more spice in the locker room. You know, there's Jeremy Pena purple this time to 250 nice one for Mark and the Astros. We already got too many. Too many low key guys, you know, Trey Turner. It's kind of a chill dude. JT, Clayton Kershaw, Freddie Freeman, Mookie Betts are all kind of kind of a little too chill. I think I think there needs to be some younger guys in there just to kind of, you know, just to kind of shake things up a little bit. Just to add a little spice and little fire, you know. There are a lot of youngsters on that Phillies team that went to the World Series. They had a little bit of spark, a little bit of fire. I think that's kind of what I'd like to see. You know, start weaving in some more of the, I mean, far farm systems, all that. I'd like to see a little more of those guys, you know, end up with a team and given starting roles and be like the Trey Turner of the future. Let's find the new Trey Turner. Let's find the next Mookie Betts. You know, let's find the next and develop and give opportunities to the next Freddie Freeman, you know, that kind of thing. There's James Wood. Ooh, he's a candy. 47 out of 2.99, Speckle. And that's a one per case at Luis Rodriguez. Sometimes we've seen two. It's like a little indentation right there, maybe from the factory. There might have been a little, little machine part that looked like it crimped that area there, unfortunately. But Edward, that'll still go to you. Sometimes we've seen two in a case.  Miguel Vargas. If you ever watch any of the, any of the, any of the, you know, Miguel Vargas. If you ever watch any of the behind the scenes Dodger stuff on the Dodgers Network, they did a little profile on Miguel Vargas. And he seems like a really, really fun guy. Real chatterbox. He talks a lot. You know, can hit really well.  To maybe take over third base. Maybe play a little outfield. He can hit well.  Kind of like Alex Ferdugo. Alex Ferdugo had, you know, a bit of that, that spark to him.   Kept people loose, kept people hungry. I think you kind of need that. Like a Kurt Gibson. You know, like Kurt Gibson getting into the 1918, providing that, that spice, that spark, that fire. Maybe with some young guys. Yeah, Jorge saying, yeah, Vargas 100% needs to get the chance to step up. He should be. Right. I'd love to see Cartaya get some time. I mean, I don't know, you know, I'm sure, I'm sure we'll try to retain Will Smith, but maybe it's time to, maybe Austin Barnes gets eased out a little bit. Maybe Cartaya gets a chance to, to get some reps, you know. There's Michael Burroughs. It's for the Pirates. Jacob, Last Bot Mojo strikes again. And I think that's what the, you know, last season was very disappointing, but Dodgers really haven't needed to do too much. They don't need to do too much. They maybe needed, need to get some fresh young blood involved in the team, right? Especially on the offensive side. I'd like to see maybe another starting pitcher get picked up either via free and see your trade. Most likely trade and get up another, another young pitcher, maybe a guy, like a Gavin Stone or a Bobby Miller. Let's see which one is going to be that fourth or fifth starter. You know, maybe, maybe add another quality arm, replenish the bullpen and kind of anoint a proper closer, maybe. I know the Dodgers like to goof around in that closer spot, but I think when you get to playoff time, you kind of need a more defined, more of a defined role, I think. So Freddie Freeman refractor to 499. Yeah, would have loved to have Luis Castillo. That would have been a nice pickup. In retrospect, that would have helped a lot, I think. Ooh, an out of five autograph. JT Schwartz. That's for you, Edgar.  And out of fives and under. Get the train whistle, Edgar. All aboard the Big Hit Express. Nice. Let's see what this guy, this guy's a local guy, kind of. Newport Beach. Fourth round pick, number 18 Mets Prospect in their organization. Groped Myring, Nolan Aronado, Chase Utley, went to UCLA. Nice, that's pretty nice. That Bobby Whit will go to Ed in the Royals. Pete Crew Armstrong, 21 out of 125. Cubs, that'll be for Michael. All right, fourth box. Jorge and Edgar, what do you think about Justin Verlander maybe being linked to the Dodgers? I'm not sure I feel about that. I know they're probably not going to commit too many years to him. But I don't know, I feel like that's like another... Do we need Justin? I'd like to maybe chase after DeGrom for like the same amount of money. You know, like shorter years, higher average annual value. Jorge, you think you'd go with Verlander for a year or two? The way you pitched last season? Yeah, I guess he still has a lot left in the tank. Right, yeah, then we don't have to face him in the World Series. I'm sure Mrs. Justin Verlander would love to spend the summer in LA. Miguel Cabrera retiring after next season, he's announced it. Is Victor Acosta, these shimmers are not numbered by the way, but all hardship. And there's Jeremy Vargas for Brian and the Brew Crew. Yeah, that's Carlos Correa, does he end up with the Dodgers? There's Edie Kapp to 150. That could be really interesting. Yeah, I think a lot of Dodgers fans don't like Carlos Correa. I mean, he'd be a good fit. The injuries concern me a little bit. There's CJ Abrams right here to 499. And there's Jojo Blackman, 220 out of 299. Speckle autograph for David and the Rangers. Got a gold, Daniel, Cueva, 27 out of 75. Yes, there are Mercedes for the twins. That'll be for Mark and the Cueva for David Nadler and the Ranger. That's to 75. More of a yellow parallel, I guess. Not gold, gold's to 50. All right, first third of the case done. Here comes the second third, middle third. I think we saw this used yesterday, but Phillies reportedly have Trey Turner as a top priority. Padres Diamondbacks among the teams interested in Xander Bogarts. And of course after Correa and Trey Turner and Xander get off the board, Danesby Swanson's a free agent too. He's not too bad either. Probably at a lower price than those other guys. Correa, twins, I think a little bit ago, had made multiple offers to Carlos Correa. So let's see if he wants to still hang around. Red Sox interested in Mitch Hannigar. National signing Franklin Beretto to a minor league deal. James Tyon drawing strong interest in free agency. All right, next one. And we got Emmanuel Valdez, 003 out of 299 for Houston. Mark with the Astros. And we got a Chae Won Bin, the South Korean to 199. Torkelson. All right, so Tork going to Matthew and the Tigers. Hopefully has a bounce back year this year. This upcoming season. Cardinals will get the Korean player. That's Mark, Wander Franco, go to Edward. Spencer Torkelson being a former number one overall pick. I've got to think that he has a longer leash. And we got Jack Herman, 135 out of 499. Refractor autograph for Jacob and the Pirates. Last spot, Mojo. 70% of the time, 100% of the time. Second auto for the Pirates. Matt Manning. Not numbered, so this must be a variation. Nice. Tigers. It's going to be for Matthew. Usually the fractors are numbered at 499. All right, next box. Wow, baseball news. Hall of Famer, Gaylord Perry. Passed away today at 84 RIP. Two-time Cy Young Award winner. And he used his creative pitching arsenal at the CornerMLB.com to win 314 games, strike out over 3,500 batters over his 22 Hall of Fame career. Third ballot, I want to say. You would trade the farm for Otani. I think we can just wait. We can just wait for that one-year extension that Otani sign, way to near. And if the Angels don't make it to the playoffs, I think you can just snatch them up in free agency. Take all that, take that trade turner money, take all the JT money, take all that and put it in the Otani boat. Next box. Got Wes Kass. 62 out of 99, Green Shimmer for Steven and the White Sox. And we've got a Juan Guerrero. 345 out of 499, Refractor Autograph for the Rocks. That's from Matthew. For the most part, the penmanship, the autos have been pretty good. Maybe he might not be on top of that list, though. There's Simon Juan. 182 out of 250. Purple Chrome for Edgar and the Mets. Yes, there are Mercedes for the Twins. Mark with that, let's see if we can find some parallels or some ink of that kid. We've got Joey Votto. 205 out of 299. Magenta for the Red Legs. John for the Reds. Jeremy Pena, rookie card for Mark and the Astros. And a Redemption. It's Christian Franklin. Chicago Cubs. Michael P. Got randomized the Cubs and the team random. Got Alani White Jr. Michael. 249. 299. Another box. According to MLB.com, we've got some rumors here. December 1st rumors. That's today. Thursday, December 1st. Thursday. Your Thursday, my Friday. John Marosi from MLB.com saying that what's chances of judges in San Francisco at close to 50-50? Aaron Judge has an offer on the table in the neighborhood of eight years and 300 million from the Yankees according to ESPN's Jeff Passen. The negotiations may only be just getting started while Passen notes at the Yankees offer could increase depending on how on how far the Giants, the other top tutor, are willing to push the market. Marosi is saying and he's not ruling out the Giants winning it out in the end. The Giants are a very realistic possibility. Marosi said Thursday on MLB Network. This is not a Yankees at 70% likelihood. And the Giants at 30. I think we're very close to 50-50. I mean, there's the Aaron Judge going back home narrative to Northern California. Giants have, I think, have wanted like a superstar kind of player in that sort of younger line-up. I hope they do drive up the prize for it. I agree. December 1st, another rumor here. Insiders Optimistic DeGrom stays with the Mets. The Yankees, Rangers, Cubs and Reys are among the teams reportedly interested in adding Jacob DeGrom this winter, however, a majority of team executives and MLB insiders indicated in a poll conducted by ESPN's Jesse Rogers that they believe DeGrom will ultimately re-sign with the Mets. We got another pirate. Specolotto, Jack Herman. 77 out of 2.99. Another pirate for Jacob. Speaking of Aaron Judge, there he is. 5 out of 99. Casey with the Yankees. There's Brian Della Cruz, rookie card, blew to 150 for the fish. That'll be for Chris.  I wonder how pissed fans would be if he doesn't end up back on the Yankees. They'd have to make another big move really quickly if they lose that on the Aaron Judge sweepstakes. There's Anthony Gutierrez for David and the Rangers. Errol Vera, refractor. 118 out of 4.99 for the Angels. It's going to be for Jacob. Won that team in the filler. Other notes do we have here from MLB.com. The Phillies have reportedly made shortstop Trey Turner, their top priority and free agency, according to John Paul Murose from MLB.com. That report was yesterday. I think we talked about this yesterday too. But according to MLB.com's Todd Zalecki, Phillies feel good about their chance of coming away with one of the star free agent shortstop this offseason, whether it's Turner, Carlos Correa's, and her Bogarts or Danesby Swanson. Trey Turner I think is number one on the Phillies list and I don't even think it's the expectation from outside the Phillies. They're going to get one of the top shortstops. Zalecki said, I think the expectation internally as well as if the Phillies are going to get one of those four shortstops. Trey Turner is probably the top of the list, but I don't think the Phillies would be too upset if they got Carlos Correa, Xander, or Danesby Swanson instead. Mitch Hannigar drawing some interest from the Red Sox and Rangers. They have a big shortstop market. Trying to look for that Corey Cedar money. Alright, next couple mini boxes here. Good luck everybody. We've got Elidela Cruz, Purple Shimmer, 186 out of 250 for the Reds. That'll be for John, a Wander Franco, and a Won Bin Cho autograph for the Cardinals. That's going to be for Mark. There you go, one of the 12 Cardinals international signings. Long Lakey athlete, five tool possibilities. They signed him out of high school. Edward with the raise gets the Wander. There's a Torkelson rookie card for the Tigers. That'll be for Matthew. And Jordani de los Santos. 38 out of 75 yellow parallel for the Pirates little color match there. Jacob and the Bucos. Another stack. There's a Bobby Wood Jr. and Evan Lee behind him. Sean Matik with the Nationals. There you go, Sean. Bobby Wood Jr. for Ed Ayrens. And the Royals. Nelson Velazquez to 499 for the Cubbies. Michael P. 348 out of 499. All right, final third of the case coming up. We've discussed this before. We'll discuss it again. I'm sure many times throughout the offseason and next season. How are the rule changes going to help certain players, especially the speedy players? You can only, I'll see the exact details of the rules, but you can only throw so many times to check a runner. A pitcher can only throw so many times to a base to check a runner, which means there's a guy with decent speed and they've been checked enough times. I mean, that could be a steal opportunity. It could be some good gamesmanship, a little cat and mouse there. Now, I think everyone assumes, oh, well, what if the pitcher does that? Is it on a Maxwell and Bates? I don't know. Could there be moments where a pitcher will waste those pick-off attempts just to dare a hitter to a base runner to steal that base? Maybe this guy thinks he's a little bit faster than he is. Maybe, you know, then you have a good catcher. You pump a fastball in there. Could be interesting. The bases are bigger, too. There's O'Neal Cruz and Antonio Pinheiro for the Brew Crew. Brian with the Brewers. There's Jordani De Los Santos, 34 out of 99. Green parallel for the Pirates. Purple pen is color black. Purple pen, purple pen. And we've got Jose Rodriguez, orange, 9 out of 25. It's for the Rangers. It's going to be for David. Got the Rangers in the filler. Rodriguez is one of the guys Yankees are looking for. Casey with that one. Luis Rodriguez, refractor. 9 out of 4.99 for the Dodgers. That's going to be for Edward. Yeah, I think the bases are growing by maybe an inch, which is a decent size change, especially if you kind of look at how many close plays there are when bases are being stolen. A lot of times, just buy a fingertip one way or the other. And I want to say that second base is moving in. Someone fact-checked me on that. I want to say the actual second base is moving in closer to the pitcher's mound by a little bit. And no shift. The shift is gone. Now some people suggest that, you know, it's not like the shift is going to help. It's not like the shift is going to help Joey Gallo. I think he's still going to strike out as much as he does, but I don't know, we'll see. Crusher weekend says, no, they're not moving second base. I don't think the 90 feet is changing. Just the geometry is changing. Oh, I guess I don't think it's happening, actually. Here's the news article. I think they're experimenting with it in the Atlantic League. I don't know what the results of that were, but the base is going to be moving inward and it will be closer to first and third base by about 13.5 inches, but the 90 feet is still the same. There's Julia Rodriguez. I think the argument is that it's not. If you look at the article, and I remember this, this was the article I read way back in the spring, the geometry is not a perfect diamond. The interesting thing about the move to move the base is it draws attention to the fact that the bases aren't actually exactly 90 feet apart. Second base is a bit off and has been for well over a century. There's Louise Guild Blue for Casey and the Yankees. So check out that article and scroll down a little bit. They'll explain the math. 3 out of 50, Danny Andrade for the twins, gold for Mark. I thought that rule was in place, but I guess they're just kind of testing it in the Atlantic League, the sandbox for all those rule changes. All right, and there's Danny, again, autograph this time for Mark and the twins. CMI Club of Good Women. Ryan Reckley for 43 out of 499. For the Giants, that'll be for Dan. I think the base has not officially moved, but they have been experimenting with it in the Atlantic League. If it does get closer by over a foot, that's going to encourage a lot, should in theory encourage a lot more base running activity. Do we get back to the glory days of base stealing? Back in the day, I feel like they were, which sounds crazy to me now, but back in the day, I feel like there were guys, individuals stealing over 50 bases a year, almost 100 bases a year. We got Robert Hassel. That is to 199, Fuchsia or Magenta Shimmer. And there's Dylan Dodd, 48 out of 100 atomic refractor for Casey and the Braves. Got Anthony Volpe, 18 out of 125, and Christian Pache, 98 out of 150 for the A's. Blue parallel for Oakland, it's going to be for Donald. Volpe goes to Casey and the Yankees. And we've got Ricardo Cabrera, John with the Reds. And yes, there are Mercedes, 166 out of 199. Nice one for Mark and the twins. All right, ladies and gentlemen, final box coming up, final two autographs. We've got another case of hobby in the store. Right now, jaskyscasebreaks.com. So if you're watching live, go and snag some teams. With a little luck, we might be able to run another one back tonight. We also have Bowman Chrome HCA Choice Edition back on the menu. So go to jaskyscasebreaks.com and gobble up some teams there. There are a lot of people hungry for some HTA. These last two mini boxes, we'll do a quick little recap. Go through some more orders, probably take a quick little break after that, and then we'll go from there. What's happening in this Thursday night football game here? The bills are up 10-7? Yeah, bills are up 10-7 in there. They're driving, they're in the red zone, second in goal. Ryan Reckley, 184 out of 499. Refractor autograph from my rivals, the Giants going to Dan. San Francisco. They're Luis Angelacunha, Ronald's brother, 2-17 out of 2-50. We've got a Christian Vecchero for Sean, Sean M. The Acunha goes to David and the Rangers and the Curio, Jason Curio goes to Cleveland. That's for Jacob. Touchdown to Gabriel Davis. Aaron Ashby, there's a flag. 22 out of 499. Brewers for Brian. Julio Rodriguez and our last auto Jorge Barossa. That's for Jacob and the Snakes. Dimebacks. What else do we have here? There's Juan Yepes to 199 for the Cardinals. That's going to be for Mark. Julio Rodriguez, rookie card. Going to the Mariners. That'll be for Sean. Last little bit here. Cruz, Harry Ford, and Royzmar, Quintana. And there you have it. And it was a touchdown. The flag was on the defense. Holding, penalty decline, touchdown stands. Extra point is good. Here is the recap for Picker Team 15. Thanks for watching. Thanks for getting in, everybody. Love all the different color there. A lot of good, great prospect hunting. Hopefully all future stars. What we're hoping for. There's the case hit. And we had, in the middle of that break, that nice JT Schwartz. One out of five. Nice one. There you go, gang. I'm Joe for jaspyscasebreaks.com, and I'll see you next time for the next Bowman Chrome Break. Bye-bye.