WEBVTT

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We can start with the next call.

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So actually it's going to talk about some of the open search tools that the end is

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in my writing for Magnostic.

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Thank you, Rosanto.

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Hi everyone.

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So my name is Arshit.

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I'm from India and I work with Kubrick as a quantum software engineer.

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And today I'm going to be talking about open source tools for platform,

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agnostic, quantum computing.

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That's a lot to take in.

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So we're with me.

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And it's going to be a little bit similar in the talk that we had just now,

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with QMAT, but a lot more than that.

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So let's get started.

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So the agenda is threefold.

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First, we're going to be talking what exactly is Kubrick.

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What are the motivations in why we exist?

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Then we're going to be diving into the open source tools,

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which we have read.

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The first and the foremost is the Kubrick SDK.

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In this, we have two components.

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The Kubrick runtime and the Kubrick transpiler,

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which enables users to develop platform agnostic algorithms.

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And then finally, I'm going to be talking about a relatively new tool.

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That's called piecasm, which is, you know,

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transformation analysis of quantum assembly language with Python.

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So let's get started.

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So first of all, let's understand why Kubrick.

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And imagine your user, and you're trying to get into quantum,

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or you're already in quantum, right?

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And think of your initial days.

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So what do you do?

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You learn a bunch of theory.

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You learn a bit of linear algebra or some quantum information signs.

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And then you want to actually run your quantum circuits,

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first on the simulator, and then on the hardware, right?

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How do you get access to that hardware?

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And let's say once you've learned a particular package,

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how do you, you know, run your circuits to different hardware, right?

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You know, the problem that are found are also face like,

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I would say, four years ago.

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And that is how Kubrick was born.

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To be a one-stop platform, enabling quantum computing,

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and not having as, you know, the last speaker said,

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vendor lock-in, right?

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So this is one feature that we have.

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That's called the Kubrick lab.

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What Kubrick lab provides is quantum software development

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in the cloud itself.

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And I'm going to be showing a quick demo after this slide.

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And you can, you know, pretty easily start working

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with any particular quantum computing package,

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like Kiskit or Amazon's bracket or Serk or Google Serk.

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And also use Kubrick SVK, which is our open source tool,

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to, you know, integrate your quantum jobs on different hardware.

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Okay?

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And let's have some moving pieces now.

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So this is a demo.

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This is, this is what you, you know,

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live at when you land at Kubrick.com.

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And whenever you click start now, this is our account page.

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This represents what kind of plan you have,

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how many credits you have.

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And for the purposes of this demo,

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I'm going to be talking about three services,

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quantum jobs, devices, and lab environment manager.

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Why?

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Because they use the open source tools,

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which I'm going to be talking about, post this demo.

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Okay?

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So first, let's, you know, talk about quantum jobs, right?

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So let's say you have any particular vendor,

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you want to execute your quantum circuit on.

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You can see that through this interface.

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This is called the quantum jobs interface.

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And you can click on any particular job,

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and see the details regarding how many Kubricks

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were present in that job, or, you know,

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what was the circuit depths,

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whether they were any measurements or not in that particular job.

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And the details about the provider on which this job was run.

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Okay?

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This is regarding quantum jobs.

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Then comes the devices.

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So we have a lot of integrations with different vendors,

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which provide their simulators and hardware through our platform.

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So for the sake of this example,

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I will, for the sake of this presentation,

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I'll take the example of AWS's state vector simulator.

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Here what you can do is that you can just,

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get some details about the device,

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and the computing, the cubits, and the availability of the device.

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And what is the form of access?

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So here it's bracket, that's the package, which AWS has developed.

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And next, we're going to be talking about the lab environment manager.

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That is present in the cubrid lab environment,

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which is launched right now.

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That is a feature which allows you to, you know,

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develop Python virtual environments directly in the cloud

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and persists them across your sessions.

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So this is the lab interface, and this is very much like Colab,

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but a lot more tuned towards quantum computing.

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Okay?

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This is the lab environment manager.

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You have a lot of environments pertaining to a user.

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And you have a lot of environments,

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which are publicly available for any user to install and work with.

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This is what I'm scrolling right now.

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Let's take an example of, let's say, Penny Lane.

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This is Zanadoos environment and we click Install.

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That will be available for any user in their personal environment,

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which you can see here.

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And you can just click on it, activate it, and there you go.

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You can start using your Jupiter notebooks right with this environment's kernel.

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Okay?

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I already have Cubid SDK installed, and we can just add the kernel for that particular environment.

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We can click on that notebook and run with all the good stuff present in that kernel for that notebook.

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So this was kind of setting the background for the tools.

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And the most important ones are Cubid SDK and Pycasm.

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And let's talk about Cubid SDK now.

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So as you saw that Cubid SDK is a tool, which allows users to not think about how they're going to do something.

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But mostly about what they want to do.

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Let's say a user wants to write a quantum program.

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They only think about what's going to be the algorithmic details,

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but not the implementation details.

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So that is like the core motivation of Cubid SDK.

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And yeah, it's available on Pyp.

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You can install it, you can want to be turned.

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So yeah.

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And the feature right now I'm going to be talking about is Cubid runtime.

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What this does is, you know, material for after we talk about what the problem it's solving.

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Right?

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Let's first imagine that you are a user and you have a high level programming language.

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And you want to execute your program on a quantum computer.

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How do you do that?

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Well, it's not very trivial.

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You need to, you know, go through converting a program into an IR or an intermediate representation.

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Go through the, you know, stuff of client side management authentication.

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So on and so forth.

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Then billing and then finally executing on the hardware and then getting back the results and transforming them.

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Right?

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This is not very easy.

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And this is why we have built Cubid runtime.

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What it does is that it will take your program.

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It will convert that to device specific constraints,

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such as the gate set of a device or what kind of topology you have.

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Then finally it will convert that to a quantum program IR,

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which can be opened for under assembly language or it can be,

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the LLVM IR that is the QIR representation.

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And then finally you have the client side authentication,

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which is built into Cubid, which interacts with the Cubid API on the backend.

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So this is, you know, about Cubid runtime.

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And now we will see another demo of Cubid runtime.

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This is just another condensed information.

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So, okay.

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All right.

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So I have a Jupyter notebook running locally.

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And just to highlight that this is all local.

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You can also prep install Cubid and start working on this notebook.

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Right?

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Given you have set it up correctly.

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So yeah, you can prep install Cubid.

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I'm not going to be doing that here.

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You can print the version.

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There are some extractions present here such as the provider or the device.

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You're trying to run your program on.

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Provider here is Cubid because we are hosting all of these simulators,

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which you are, you know, seeing here.

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Okay.

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And then we can choose for the sake of this demo,

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the AWS is straight vector simulator.

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And you can clearly see that the target specification,

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or the program type it expects is bracket,

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which has been developed by AWS.

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And for here, we're just going to be preparing a GFZ state.

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This is a very simple max million times in the state.

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And you will set a short.

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I mean, a key point to note here,

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we're going to be building in packages,

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which are not directly supported on AWS.

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Here we're building in search, which is a Google package.

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Post that will build and kiss it.

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And then send directly to the AWS simulator,

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so that we can, you know, see how good Cubid SDK is.

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This will take some time because obviously this is interacting

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with the actual AWS packet, but I will end here on the jobs.

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And just to explain a little bit,

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this is the simulator object.

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We run the circuit, and hey, we got results.

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Okay.

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And these are the probability of our results.

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Similar case is with the kiss kit,

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circuit, we again take the AWS simulator,

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run the kiss kit, circuit, get the results back, and display them.

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Okay, so this is just, you know, promoting the efficacy of Cubid,

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that we are actually using different packages to run

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circuits on different devices.

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Okay, getting back to the presentation.

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All right.

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So yeah, as you saw that this enables agnostic quantum development.

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You can code in any package and execute on any backend with Cubid.

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And you can also see our runtime user guide, which we have on docs.

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We currently integrate with kiss kit,

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a Microsoft Azure, then AWS, OQC,

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IonQ, they're also other providers as Cubera,

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and any C, which are integrated,

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but these are one of the foremost.

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And yeah, this was the demo for runtime.

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Now let's go behind the scenes.

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How do we do that?

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So we have something called a Cubid Transpiler,

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which detects and makes your circuits

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run-able on a quantum device, right?

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Let's dive into the details of that.

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It allows you to convert one package to another,

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transform your, you know, program to device specific requirements,

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and then finally also enables access to any optimizations that you have

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in a particular package, right?

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Because that's integrated with our SDK.

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The model is similar to a graph.

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I think most of you are familiar with the graphs in the redesigns.

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Every single node we have is a quantum computing package,

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and an edge to another package is a conversion path.

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So let's say we choose search,

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and you want to convert to, let's say, kiss kit.

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We can go through search, bracket, kiss kit,

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or we can choose to cancel and do to kiss kit.

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So this is our Transpiler model.

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This is our works behind the scenes.

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And a test, do ensure that there is unitary equivalence,

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or basically there's no loss between converting one particular circuit

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from one package into another,

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and we have a very robust unit test for that.

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And you can easily execute as we saw a kiss kit

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so we can get on an Amazon back.

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So you can also see code here, which is present in our bit of repo.

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And this is like the example for the conversions.

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Let's say you take kiss kit, which is IBM's quantum computing package.

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You call Cubrid or Transpile on it.

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You take the circuit, you take the target,

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and then you can, you know, Transpiler,

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circuit to let's say AWS,

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search, open gasm, penny lane, or anything else.

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Again, this is our demo node book,

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which you can, you know, see to just see this in action.

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So flag that.

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Now, finally, we also have a pretty vibrant community,

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and we have a lot of issues and help required in Cubrid SDK,

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and other open source tools.

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And we also participate in Unitary Hack,

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in which contributors can actually, you know,

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submit PRs to different issues that we've registered for the Unitary Hack.

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And currently, we also have part of the QOSF mentorship program,

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which is a quantum open source foundation,

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where there are different cohorts for different mentees

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to participate in and develop cool projects.

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So, yeah.

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And now, let's talk about PyCasm.

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So, PyCasm is a tool, which we have recently developed

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for, you know, open gasm program transformation and analysis.

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And this is also a PayPal installable.

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You can, you know, play around with it.

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You don't even require any kind of, you know, access from Cubrid

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to interface with it.

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So, let's think about motivations first.

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And for those of you who don't know, open gasm

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or open quantum assembly language,

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is one of the most popular IRs in quantum.

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And that the effort has been, you know,

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undergoing with IBM lot.

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And gasm version 3.x, which was released, you know,

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in recent years, has a very extensive grammar.

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So, it is almost like Python, if you see it.

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And there are tools with support,

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type check-in, and semantic analysis.

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But there is not a tool with support,

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comprehensive semantic analysis.

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So, for example, if you see here,

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this is an open gasm code segment.

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You can see there are functions involved.

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There are Cubrid declarations involved.

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There are loops involved.

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Then there are, you know, types involved.

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How do we actually make this open gasm program,

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runnable on a quantum device?

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Because those devices just support, you know,

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let's say the Clifford set.

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Or let's say the RZ plus Cx set.

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So, something like that.

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Sorry, RZ plus Cx.

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So, this is the main motivation.

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And currently, we have extensive support for each of these features,

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which are, you know, present on this, and more.

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And a feature which I'd like to highlight is program

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unrolling and validation.

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So, by unrolling here, I mean, let's say,

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we have a very large program,

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and we want to decompose it down into sequential instructions,

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which a quantum computer can execute.

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That is what unrolling is.

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And we also have some support for transformation analysis,

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wherein you can, you know, plug in your quantum program,

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the open gasm program.

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Then, you know, start analyzing, like,

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how many number of Cubrids should we have,

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what's the depths of the program?

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Do we have barriers or measurements or so on and so forth?

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This is the complete directory,

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and just spit out errors if there are n.

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So, that was about pikasm.

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And these are the references.

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These are, you will have to access the slide

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because these are links.

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It didn't add the full URL fair.

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And these are the contact information.

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Thank you.

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Thank you.

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Any questions?

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Actually, actually, I have a Q and I'll see you next week.

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Yeah.

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Yeah.

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Yeah.

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So, initially, what we came from was a hub and spokes model.

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Oh, yeah, sorry.

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So, he's asked that the transpiler,

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why is the transpiler model going on?

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Yes.

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So, initially, what we came from was a hub and spokes model.

16:14.000 --> 16:16.000
Oh, yeah, sorry.

16:16.000 --> 16:19.000
So, he's asked that the transpiler,

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why is the transpiler model the way it is?

16:21.000 --> 16:24.000
And like, it might be that there's a lot of work

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which has been put into it and it can be

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probably simplified, right?

16:28.000 --> 16:29.000
From not wrong.

16:29.000 --> 16:31.000
So, initially, what we had was a hub and spokes model.

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Where the center there was chasm and every, you know,

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every provider went through chasm and then

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through the other package.

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Something like a bicycle wheel, right?

16:40.000 --> 16:41.000
Yes, folks.

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But what we found that our native converters

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couldn't actually match those speed of conversions,

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which was present in direct converter.

16:49.000 --> 16:53.000
So, for example, let's take the example of, let's say

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bracket to cascade, right?

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So, if there is a package which directly converts

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bracket to cascade and back and back,

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then we couldn't actually use that speed or, you know,

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their functionality.

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So, what we tried to do is that combine, you know,

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the best of both worlds and develop a graph-based model.

17:12.000 --> 17:15.000
And this is not complete because the edges also have weights.

17:15.000 --> 17:18.000
So, if the, you know, if the, if we can really not exactly

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what we do, is that we try to find out decomposers

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in the current package, decompose the program,

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and then try to convert, you know,

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one-to-one map from the source to target.

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And that occurs loss.

17:31.000 --> 17:33.000
So, that means the edges are actually more weighted

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if we do not have direct converter.

17:34.000 --> 17:36.000
So, something like that.

17:36.000 --> 17:37.000
Thank you.

17:37.000 --> 17:40.000
Any other questions?

17:40.000 --> 17:43.000
Any other questions?

17:43.000 --> 17:44.000
Any other questions?

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Any other questions?

17:45.000 --> 17:46.000
Any other questions?

17:46.000 --> 17:48.000
Any other questions?

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Any one more.

17:50.000 --> 17:52.000
Any one more question?

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Any one more thing?

17:53.000 --> 17:54.000
Any other questions?

17:54.000 --> 18:02.000
Any other questions?

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Any other questions?

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Any other questions?

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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Yeah, you can use that because.

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.

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Yeah.

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.

18:23.000 --> 18:25.000
So as Andrew has said, can we directly use the

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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..

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.

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.

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Yeah.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.