Back to the Source: Why YOU Need TARTLE to Win at Data

Alexander McCaig (00:10):

Okay, everybody. Welcome back to TARTLE Cast. Thank you to everyone who has been an avid listener since day one. And to those who are joining us now today, you're coming into an interesting episode about Innovative New Algorithms Advance the Computing Power of Early-Stage Quantum Computers.

Jason Rigby (00:26):

Quantum.

Alexander McCaig (00:27):

What does quantum mean to people? I don't think people really know what quantum is supposed to-

Jason Rigby (00:31):

I think people understand quantum computers, if you're halfway techy.

Alexander McCaig (00:34):

Yeah. I think they know it's fast.

Jason Rigby (00:36):

You've got to get the idea of these super big, huge computers.

Alexander McCaig (00:38):

Yeah. Do you remember the days when IBM used to take up a whole room or multiple rooms, a whole floor of an office building just for a computer to crunch basic arithmetic and it had punch cards?

Jason Rigby (00:48):

And it had those little...

Alexander McCaig (00:49):

Yeah, exactly. Tumblers and shit. Well, the thing that's taking up the space now are the quantum computers. It's not that they need a whole bunch of electrical space. They need a whole bunch of space to cool something. For these quantum computers, here's a whole idea.

Today, when we go to compute something, it's a linear process. You can't compute multiple things at the same time. Yes, our processes are fast. Yes, it's quick, but it's printing zero and one, zero and one, binary code one after another. With a quantum computer, it moves away from the linear aspect of computing where it has to do one thing before it does next. It has the ability to do two things at the same time. Instead of printing a zero or a one, it can print zero and one at the same time.

Jason Rigby (01:36):

Yes. That makes sense. Yeah.

Alexander McCaig (01:37):

It's ability to do complex complications. Complex complications?

Jason Rigby (01:44):

Complications. Yeah.

Alexander McCaig (01:45):

What the hell is the word? Calculations. Complex calculations. Its ability for it to do that is enhanced. The reason for this is because in physics, at a quantum level, when you're trying to actually understand material science or things that are happening in physics, you have things like spooky entanglement. Two things can be affected at the exact same time, but they're totally independent of one another.

Jason Rigby (02:05):

Yes.

Alexander McCaig (02:06):

The quantum computer is taking fundamentally these principles of entangling, your zeros and ones, and seeing how that can actually foster new calculations. Calculations that were attributed to understanding string theory or the way atoms and electrons work, anything of that nature, is now being put into computers. It can be modeled appropriately at an effective speed. Because to do that modeling with our old way of computing is not efficient. Let's get into the article.

Jason Rigby (02:36):

I mean when you talk about computation and calculations, I think this is what's really important. I watched a show on this scientist and it's Y-O-N-G X-I-N Y-A-O. It's because I don't want to pronounce it.

Alexander McCaig (02:47):

Yong-Xin Yao.

Jason Rigby (02:48):

Yeah. And his research partners, Ames Labs, use the power of advanced [inaudible 00:02:52] to speed discovery in condensed meta physics, which we talked about. This is what I think is really interesting. Current high performance computers can model properties of very simple, small quantum systems, but larger, more complex systems rapidly expand the number of calculations a computer must perform to arrive in an accurate model. Slowing the pace, not only of computation, here's the difference, but also discovery.

Alexander McCaig (03:14):

Yeah. Because the actions and reactions which happen at molecular levels or anything of the sort, it's very, very difficult to keep up with. In the atom itself, there's so much information just packed into one atom. Now, when I begin to combine these things to create certain molecular elements or bonds, the amount of information, astronomical amounts of information come out of those structures.

Your discovery for finding new materials, say, for instance, I want to create a new type of metal. But I want to essentially model how these two metals fuse together. Or I want to understand how these two chemicals fuse together. That's very difficult to do with our current computing systems. If we can enhance that speed, the discovery of which we advance our material, science will grow tremendously.

The whole focus of this here is let's use these early stage quantum computers, taking up big rooms, keeping them super cold, to actually run advanced algorithms that can help us understand these large molecular groups.

Jason Rigby (04:23):

Yeah. And I want to get into a little bit of detail on the algorithm itself, these new algorithms that they're making. "They tap into capable existing quantum creators." We know this. "And then tailor a number in variety of educated guesses." That's in quotation marks, educated guesses. I mean, this is physics here. "The computer needs to make an order to accurately describe the lowest energy state," because that's a problem. We're having issues with energy, it's the same scenario with Bitcoin.

Alexander McCaig (04:52):

Yeah. When you're mining.

Jason Rigby (04:53):

Yeah. When you're mining. I mean you're using mass amounts of energy. People don't understand this and just with Bitcoin real quick. The lower the energy, the easier it is to hack. The more energy there is, the more time it takes. It's just a simple first principles.

Alexander McCaig (05:10):

It's a simple thing. Where if I want to create new material objects, I want the lowest energy state. I don't want to have to run multiple nuclear power plants to fuse a new metal. Do you see what I'm saying?

Jason Rigby (05:20):

Right.

Alexander McCaig (05:21):

I want to understand bonds at the lowest energy state. It's easier for humans to deal with.

Jason Rigby (05:26):

Yeah. "The lowest energy state in evolving quantum mechanics of a system." But this is cool with the algorithm. "The algorithms are scalable, making them able to model even larger systems accurately with existing, current noisy." And what he's talking about noisy there is, "Fragile and prone to error, quantum computers and their new future iterations."

Alexander McCaig (05:43):

Here's what happens here. If you go to extrapolate an algorithm, whether in a predictive sense, educated guess, whatever you want to call you, as it gets to the fringes of the calculation, it breaks down. This happens a lot in advanced mathematics. Unless you tailor that back to a fundamentally scalable algorithm that aligns with how things in quantum mechanics happen at a fundamental level, now you have something scalable. Regardless of the size it's efficient within that processing itself. It won't essentially deteriorate as it goes to the branches of the tree. Does that make sense?

Jason Rigby (06:13):

Yes. No, that makes sense.

Alexander McCaig (06:14):

Great.

Jason Rigby (06:14):

Yeah. And then he says, "Accurately modeling spin, a molecular system is only the first part of the goal." Said, yeah, "In application, we see this as being used to solve complex material science problems."

Alexander McCaig (06:23):

There we go.

Jason Rigby (06:23):

"With the capability of these two algorithms." Here's the outcome that they're hoping for. "We can guide experimentalists in their efforts to control materials' properties, like magnetism, superconductivity, chemical reactions, and photo energy conversion."

Alexander McCaig (06:40):

Yeah. Okay. Here's some cool stuff to think about. If I can control magnetism, what does that mean? I can float around.

Jason Rigby (06:49):

You can control the world.

Alexander McCaig (06:50):

You control anything. And they understand that. Why do you think they put that first? Second one, super conductivity. Superconductors help with battery capacitors, storage, anything of that sort. Think about their list of priorities for the department of energy.

We got to control magnetism, so that we can have anti-gravity. We need better storage capacities with our superconductors. All right. And then chemical reactions so we can create those new materials, which would support those batteries. And then the last one, photo energy conversion. When our things are floating around in our charged Ionosphere, sucking up electrons, we want to make sure that those materials are readily charged by what is readily available.

Jason Rigby (07:27):

Yeah. And he says, "Our long term goal, so here we go, "Is to reach quantum advantage for materials, to utilize quantum computing to achieve capabilities that cannot be achieved on any super computer today."

Alexander McCaig (07:38):

Yeah. Here's the interesting part about the quantum. This is kind of cool. It's very, you can't put the cart before the horse here. But it's like, I started with a stone tool. And I'm trying to cut stuff and it's not very good. Or in surgery, they used to use obsidian glass. And then they moved to scalpels.

Jason Rigby (08:00):

It also kills White Walkers.

Alexander McCaig (08:01):

Yeah. It kills White Walkers. And then, they moved to scalpels. And then the scalpels moved to lasers. What is occurring here is that this early stage quantum computer is like having the early tool. You actually use it to make more tools. Once they can create new materials for the benefit of the super computer itself, once that gets to its lowest energy state, then there's no holding us back. It's essentially, that's the high bar we have to get over. Is that making sense?

Jason Rigby (08:29):

Yeah, that makes 100% sense.

Alexander McCaig (08:30):

What is the key here? Data and scalable algorithms. It's what it is.

Jason Rigby (08:36):

Yeah. And that's what everybody's working on today. But the statement that they always say is the not having the computing power, the computating power and being able to harness that is the problem that's affecting it all. Just the hardware. We can make the software to be able to scale to that level. But the hardware in and of itself is not there or available yet.

Alexander McCaig (09:00):

I'm getting a little déjà vu from you right now. Was it a dream I had last night?

Jason Rigby (09:06):

Of our long term goals of quantum advantage with Tartle?

Alexander McCaig (09:09):

Yeah. Something is-

Jason Rigby (09:10):

Having data algorithms that...

Alexander McCaig (09:12):

Are just astonishing.

Jason Rigby (09:14):

Well, I mean, we do first principles thinking. We go back to the source.

Alexander McCaig (09:18):

We go as far back as it gets.

Jason Rigby (09:20):

That's as simple as...

Alexander McCaig (09:20):

Let's look at the basics. You know what I mean?

Jason Rigby (09:23):

Yeah. We don't need quantum algorithms.

Alexander McCaig (09:25):

Well, I think that's what they're learning here.

Jason Rigby (09:26):

To share intent.

Alexander McCaig (09:27):

They've gone all the way out on the fringe. They've tried to apply their stuff, but they realize it breaks down. Which means they have to fundamentally go back and take a real hard look at the thing that's supporting it all.

Jason Rigby (09:37):

If you're a business and you want to sign up for Tartle, because I think this is really important. A lot of businesses don't understand. They just are not introduced to Tartle, one. Or number two, they don't understand the capabilities.

Alexander McCaig (09:49):

Or the value of data.

Jason Rigby (09:50):

Of what Tartle can do for your business specifically. Let's use natural gas industry. Let's just pick that one.

Alexander McCaig (09:58):

That's a great example. Here's the cool part. Local natural gas company here in New Mexico, you could probably figure out who they are, sends out paper surveys in the mail. And they hope that someone's going to take the time to diligently fill out this 15 question survey.

Jason Rigby (10:16):

There will be one certain demographic that would.

Alexander McCaig (10:19):

That will do that. And then mail that back to them. And then that's how they presume the safety education around gas pipelines is in the United States. By running that survey via paper that comes via junk mail, okay, to your mailbox, which you're going to throw away. They think that is the key driver.

Jason Rigby (10:37):

That's expensive too.

Alexander McCaig (10:38):

Oh, it's very expensive. Now, that has a very low response rate and also a huge carbon footprint. Okay. Oil and natural gas, you already have a huge carbon footprint. Let's slow that down for a second and let's increase that response rate.

For them, digitize the survey to collect that data directly from the individuals who are participating in that region with oil and gas. Interesting. Oh, wait a minute. And they can embed educational videos to teach the person and then collect the survey data from it. And then establish relationships to go back to them to say, "Now that this time has passed, let's ask you some followup stuff."

The power of that for the decision makers in these corporations who are actually educating on safety by federal law, dictates that they have to do that, expands dramatically. And educating the public is number one. Number one, always. Because if they're not educated, people become agitated and they turn against you.

For your benefit as a business, whether you're in natural resources like that or you're Walmart, all companies are tech companies. We ask you to take the step with Tartle so you can establish those relationships with the people you serve.