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Impact Quantum: A Podcast for Engineers - Data Driven Media EPISODE 1, 14th August 2020
Quantum is Coming
00:00:00 00:52:14

Quantum is Coming

The very first episode of Impact Quantum.

Watch the livestream

https://www.linkedin.com/posts/frank-lavigne_quantum-compute-is-coming-activity-6700060953964232704-8JwD

Transcript (AI Generated):

This episode of the impact quantum podcast is rated one schroedinger and is entitled Quantum is coming. 

Speaker 1 

I'm Frank Lavigna an this is the very first episode of impact quantum quantum computing is about the radically change. 

Speaker 1 

Uh, how we live an work, so let's roll the intro. 

Speaker 1 

Alright, with me to my I think it's left on the screen. Here is my cohost from data driven Andy. Later, how's it going, Andy? 

Speaker 2 

Hey Frank is going awesome. 

Speaker 2 

How are you? 

Speaker 1 

I'm doing well doing well. I've got the I've I've got the fever. 

Speaker 1 

Do you? It's not covered. It's not Corona Squantum Fever. That's what I got. 

Speaker 1 

So Andy and I have another podcast data driven.tv where we explore the emerging fields of data science machine learning an artificial intelligence, and you're probably wondering what the heck are you data sciency data engineer guys doing here talking about quantum computing? 

Speaker 1 

For miles back, let me tell you a story. In November of 2019 this is back when people would actually travel places. 

Speaker 1 

And congregate in large groups without thinking twice. This is before the words Wuhan meat market. 

Speaker 1 

More commonplace before when Corona was just a beer I attended a conference called M labs or might machine learning and data science summit. It's an internal only kind of conference for Microsoft employees thrown by Microsoft Research where they kind of come out of the lab and they talked to us about kind of what's around. 

Speaker 1 

Pike 

Speaker 1 

And, um. 

Speaker 1 

This is the conference that I went to in 2016 where I had my ahha moment or you know about getting into this field right? I kind of. I was into data visualization and power BI and kind of Munging Data with Excel. 

Speaker 1 

But the engineering wasn't in me, was not satisfied. Then I discovered machine learning and artificial intelligence, and where that was headed. 

Speaker 1 

And when I came back from that conference, completely switched on about. This is the future. This is coming. It's here, it's you know it's it's on its way. 

Speaker 1 

Most people thought I was crazy. 

Speaker 1 

I mean, even I had my doubts about it, so yeah. 

Speaker 1 

Through 

Speaker 1 

A number of coincidences. It took me 4 or or other reasons, took me about four and a half years or 3 1/2 years to get back there. 

Speaker 1 

Um and the first day came and went. I didn't have that. Ah ha moment, so I'm like, well, you know, maybe that's just like a once or twice in a lifetime type. Ah ha moment. 

Speaker 1 

It happens, doesn't have sure sure. 

Speaker 1 

And. 

Speaker 1 

Then on the second day it was a hardware presentation. Now I can't talk about everything I saw, but that's when quantum computing was explained to Maine. 

Speaker 1 

And why? 

Speaker 1 

It is the next big thing. 

Speaker 1 

An I knew I was on the right track because when I came back I started telling other people that quantum is coming quantum quantum quantum. 

Speaker 1 

I sat it again like a ranting lunatic. 

Speaker 1 

And that kind of told Maine. 

Speaker 1 

That maybe I'm on to something because the next wave always looks crazy before it happens. 

Speaker 2 

Yeah. 

Speaker 2 

So. 

Speaker 1 

With that in mind, I attended, um I started studying quantum type computing articles and and Whatnot, and I even installed Q Sharp that night in the hotel. 

Speaker 1 

Then I opened up my. 

Speaker 1 

Brought on my Visual Studio. 

Speaker 1 

And, um. 

Speaker 1 

Was like OK, now what? 

Yeah. 

Speaker 1 

Um and once again, I'm trying to keep a growth mindset here when I first read a book on statistics. 

Speaker 1 

My first reaction was, uh, what did I get myself into? 

Speaker 1 

Right? 

Speaker 2 

But that's you know that's not a common Frank. A lot of people come into this an you know I don't. When you're starting out, who knows everything you're gonna learn and everything you gonna need to learn. 

Speaker 1 

Exactly right, and I think that there's going to be a lot of people. 

Speaker 1 

Um, that are going to be in a similar boat and. 

Speaker 1 

On our main show we kind of discussed this is that. 

Speaker 1 

It's hard for people to think back. I mean, it's hard for people to think back honestly about what life was like before Koven you know going yeah, getting into elevators like whoa other people. 

Speaker 1 

On that, at the same time. 

Speaker 1 

Things like that, but it's hard to go back even further to think that there was a time when only PHD's did data science right and then was right. 

Speaker 1 

Think about, Well, you have to go back to school like when I when I asked folks for advice on what to do at that summit actually in 2016. 

Speaker 1 

I know that one guy is like I just go back to school and get get PhD in statistics and this is before data science programs existed at universities, so this was really yeah. 

Speaker 1 

We forget how quickly this is moved. I mean now everybody and their cousin hasn't data science course and I don't mean that disparagingly, I just. 

Speaker 2 

Know there's just. 

Speaker 1 

A the Germans have a phrase. 

Speaker 1 

Um, it's been awhile, so I spoke German on daily basis. Just broke fall derval something like that was like the agony of choices and we kind of have that now which four years ago we didn't. But around three years. Three years or so ago, guy named Siraj Raval and I'll just drop his name. I won't say anything else opinion Wise had the ability. 

Speaker 1 

Not so much to conduct unique and custom research on his own. 

Speaker 1 

But he did have the gift. The true gift is true talent. 

Speaker 1 

Was explaining these complicated data science machine learning and artificial intelligence concepts in simple ways. 

Yeah. 

Speaker 1 

And it is a rare gift that if you can understand something, you can explain it now. Richard Feynman, a noted physicist. He says if you can't explain it, you don't understand it. 

Speaker 1 

Or you can't explain it simply you don't understand it. Something like that. I totally butchered that. 

Speaker 2 

Now you got it, yeah? 

Speaker 1 

So the key here and the goal for this show is going to be just that, like the ability to explain this. 

Speaker 1 

And, um, in in in a clear and simple way that engineers can understand. Maybe not your Grandma. 

Speaker 1 

Unless your grandma happens to be a particle physicist. 

Speaker 1 

It happened, I had a great aunt who was sister to my grandmother who was an engineer which back when in the 40s and 50s was a woman engineer was was quite an accomplishment in the military, no less too. 

Speaker 1 

And it was not Grace Hopper, No, but. 

Speaker 1 

Um, though that would have been cool. 

Speaker 1 

In any case, um, the key here. 

Speaker 1 

Is that we want to explain this in ways that software engineers and data engineers would understand? 

Speaker 1 

And that's our goal for this show. 

Speaker 1 

And. 

Speaker 1 

Now, the best way to set up a learning scenario, which I in the last four years or so I have made learning a priority in my life, right? Yeah, I have the numbers to prove that 266 certifications since December 2016. 

Speaker 1 

So. 

Speaker 1 

That I don't say that to show off, I just showed that to saying like I got. You know, it's just become who I am now. It's become a habit. Yeah, I don't know where I was going with that, but. 

Speaker 1 

The best way to set up. 

Speaker 1 

The learning here is the notion for learning is to set up a reason why right. If you if you want to learn something, you're going to be far more motivated and far more likely to capture that information and retain that information. 

Speaker 1 

And. 

Speaker 1 

So. 

Speaker 1 

Let's start there. Why why should you learn quantum computing? Why? Why is this? Like with the data science world is rockin, right like AI is rocking? Yeah, it is rocking. However, winter may be coming right to continue that Game of Thrones theme. And I'm going to try to share my screen. 

Speaker 1 

An if you were listening to just the audio, don't worry. I will explain this in a um. 

Speaker 1 

In a way. 

Speaker 1 

That makes sense. 

Speaker 1 

Um, hopefully. Let's see if I get that screen up. 

Speaker 1 

Alright, So what you're seeing here is an article. It's from Venturebeat they basically warn. 

Speaker 1 

Researchers at MIT warn that Dean is approaching computation. It's now what does that mean? Well, I thought the cloud had infinite scalability. Well, it it kind of does, but there is a practical upper limit that we are starting to bump up against. 

Speaker 1 

And this is kind of the thing that got my attention, so this article is from July, but I heard this in. 

Speaker 1 

Um, late last year? Kind of about it. You know this is becoming a problem and let me explain why this is a problem. 

Speaker 1 

Um? 

Speaker 1 

Because let's take GPT 3 for example. So GPT 3 is an. 

Speaker 1 

A natural language processing model. 

Speaker 1 

It took about. 

Speaker 1 

Our 175 billion parameters in right? So that was just the input level like in terms of what it what it could do. 

Speaker 1 

It took a certain amount of time to train that and it took costs somewhere between 5:00 and $12,000,000 depending on depending on who's answering the question. 

Right, yeah? 

Speaker 1 

GPT 2. 

Speaker 1 

Only only had 1 1/2 billion parameters. 

Speaker 1 

Right, yeah, that's a That's an order of magnitude greater right 175 from 1.5. 

Speaker 1 

If there was ever going to be. 

Speaker 1 

A Jeep 4. 

Speaker 1 

We're now looking at. 

Speaker 1 

Um? 

Speaker 1 

We're now looking at. 

Speaker 1 

I totally lost my train of thought. 

Speaker 1 

OK, alright thank you for handling the comments and. 

Speaker 2 

Yeah, that's what I'm I'm. 

Speaker 1 

Looking for streaming this live. I actually I might edit this part out of the RSS feed, but the I announced the show on roopesh show on Wednesday. He has an Instagram life thing going on, so I announced the show. So that gave me a little bit of extra leverage to do this. 

Speaker 1 

Um on time today, but the short of it is, is that we are hitting these limits, so you can imagine what a GPT 4 would have would have trillions of parameters. 

Speaker 1 

And I don't know if this is going to be. What's the cost going to be? Is it going to be exponential rise? But at some point we are going to run out? 

Speaker 1 

Of. 

Speaker 1 

Keep you. 

Speaker 1 

And certainly doing this at scale, we are going to run out. 

Speaker 1 

What this means? 

Speaker 1 

Is somewhat concerning. Kind of, you know, three to five years out. Is that the innovation in research is going to stop. 

Speaker 1 

Right, yeah and. 

Speaker 1 

Then, once the innovation research stops, the new products that come out that get venture funding then ultimately get bought by some of the big tech firms is going to start slowing down. 

Speaker 2 

That is going to, oh, that's what we call winter, right? 

Speaker 1 

Right, that's a I winter an if you think this is implausible, I urge you to take a look at history because this has happened before. 

Speaker 1 

And we are at the early stage of this, potentially happening again. Predicting the future is hard by the way. In case you didn't know. 

Speaker 1 

But if you. 

Speaker 1 

Look at the advancements that were made in artificial intelligence from I mean, Alan Turing. 

Speaker 1 

Um came up with the notion of the Turing test, kind of defined kind of the core principles of what we call the field of artificial intelligence today. 

Speaker 1 

This is a 70 year old we're in the 7th decade of AI. 

Speaker 1 

Right now to read some of the breathless press releases from various companies, you would not think you would think that AI started about 2000 nine 2010. 

Speaker 1 

Not true. 

Speaker 1 

But at every stage that there's been advancements in AI and I'm talking going back to the 60s. 

Speaker 1 

And even in the 80s. 

Speaker 1 

When the concept of Neural Networks were really pioneered and kind of used. 

Speaker 1 

At every time there was a burst of innovation. 

Speaker 1 

And at every time. 

Speaker 1 

The innovation outpaced compute ability. 

Speaker 1 

To keep up, yeah. 

Speaker 1 

So then that led to AI winters, so including the worst AI winter so far has been um from the mid 80s till about 10 maybe 12 years ago. 

Speaker 1 

Now, that doesn't mean that research doesn't happen in that field in the mean time, I actually. 

Speaker 1 

Took my first. 

Speaker 1 

Course in artificial intelligence in 1990. 

Speaker 1 

4 now was the 2nd semester, so it was. 

Speaker 1 

It was 1995 or 1994. 

Wow. 

Speaker 1 

And nine years began with a one. 

Speaker 1 

Right crazy there are people that are. 

Speaker 1 

Probably watching this that aren't even they weren't even live then, and my professor at the time noted researcher in the field X IBM doctorate distinguished engineer. I mean smart guy. 

Speaker 1 

But he was like, oh, this is gonna be the next paradigm. This is going to be. And basically it was. 

Speaker 1 

Prolog was a Prolog programming course. 

Speaker 2 

Yeah. 

Speaker 1 

An the final I kept waiting for like OK where's the AI coming from like where is he coming from and and that ah ha moment never came and it was just basically kind of. 

Speaker 1 

A hybrid of inference and. 

Speaker 1 

Recursion and was like that's it. 

Speaker 1 

An A lot of these expert systems that were that he was an expert in expert system, so that's very meta, right? There were essentially if you kind of Peel the covers back, they were essentially just a ton of ifdef statements. 

Speaker 1 

Yeah. 

Speaker 1 

Again, where's the AI in that, right? 

Speaker 1 

Right, so that really set me up for to not be a nonbeliever so so much the point when I saw some of the early work before they were cognitive services, this was kind of they were just still in the research lab. 

Speaker 1 

They had a DC Tech Fair which is essentially researchers coming out to DC at the Microsoft Office will kind of show off what they're working on, and a number of it was computer vision and artificial intelligence. 

Speaker 1 

And I saw this thing where he he he uploaded a picture to this program and it it was a picture of a cat. 

Speaker 1 

He was a picture of a cat. 

And. 

Speaker 1 

He uploaded it and the description came back saying, hey, this is a picture of a cat. 

Speaker 1 

And I'm like I looked. I looked at that and I was like my first part of it was like yeah, that's cool. Then my sinic kind of came out and I was like, Yeah, but it's probably some kind of weird inference recursion thing going on. 

Speaker 1 

So I kind of walked away and continue filming the rest of actually filmed the event. You can check out the video on YouTube. 

Speaker 1 

Which we will link in the show notes. 

So. 

Speaker 1 

That left me very sceptical of what the state of the art was. It wasn't until I saw kind of what the real state of the art was at a follow on event the year later, the ha moment that I had the Blues brothers moment as we've often referred to in a data driven dot TV show, so that takes us to today, 2020 or 2019. 

Speaker 1 

Is when I was there, I actually recorded a dated point right in front of the building. 

Speaker 1 

I made notes record outside because of various reasons. 

Speaker 1 

And that got my attention, Anne. 

Speaker 1 

As Luck would have it, fate, the universe or whatever, I started getting back into it a few weeks ago when this start of articles started popping up that, you know, we are dangerously close to an AI winter becausw of just the nature of how dense we can pack transistors. Moore's law may be coming to an end. 

Speaker 2 

Wow. 

Speaker 1 

And if it doesn't come to an. 

Speaker 1 

End, it's definitely not going to be the 18 month cycle or less than 18 month cycle. It's going to start taking longer now. We take that for granted, so I go ahead and. 

Speaker 2 

No, I was just agreeing with. 

Speaker 1 

You, yeah, we take it for granted. I hear my phone buzzing but I don't know where it is but your phone, here it is. Your phone has more compute power. 

Speaker 1 

Then NASA had. 

Speaker 1 

Um to send during the Apollo 11 program. 

Speaker 1 

Which let that sink in, right? You have more power than that. You're more network connectivity, right? We are spoiled. It has been. Moore's law has been in existence for decades as well. We don't know. 

Speaker 1 

What it's like to not have Moore's law? 

Speaker 1 

That is going to cause some serious problems for just computation in general, right? Not just AI, right? So in this. 

Speaker 1 

Enter quantum. 

Speaker 1 

And This is why quantum is is is coming, so to speak. 

Speaker 1 

Every major. 

Speaker 1 

Cloud provider has or is about to have. 

Speaker 1 

A quantum service, so clearly there's something going on, right? There's a lot of money being thrown at this. There's a lot of research being done. 

Speaker 1 

I am not alone in my belief that quantum is coming. 

Speaker 1 

It's the question of when is it coming and how absolutely manifest itself. 

Speaker 1 

So if you. 

Speaker 1 

Go ahead and. 

Speaker 2 

No, I'll just agree with you again an I think what you said, it's easy to gloss over what you just said about how, how used to Moore's law we are and will put a link into. You know you could search. Certainly search this up, search this on Wikipedia. 

Speaker 2 

But it's the idea that it's every 18 months that the amount of transistors we can pack into an integrated circuit doubles. 

Speaker 2 

Right, so it's an it's not that. 

Hey. 

Speaker 2 

I don't know a better way to describe it. I mean I could actually use um, you know, examples that we've all experienced from the recent pandemic? We're recording this on August 14th. 

Speaker 2 

Of 2020 and you know, we're we're dealing with the repercussions of of a global pandemic or a deadly global pandemic. And there's a lot of things now that we didn't know that we didn't know. 

Speaker 2 

Like for this hit, and it's that kind of thing more as well, it falls into that category of we don't know what we don't know, so it's it's asking the fish what is wet feel like? 

Speaker 2 

You know how do. 

Speaker 2 

We you know how? How do we incrementally think of a world in vision? 

Speaker 2 

Uh, every 18 months not doubling the number of transistors on an integrated circuit and we can't, right? We can't even envision that at this point. 

Speaker 1 

I mean, even think back to like your personal computer, right? Like there was a time in in the 90s, primarily right? You would buy a computer and the joke was it would be obsolete before we even took out the box, right? 

Speaker 1 

Right, because you know the Pentium 90, you know Pentium 90 and then two weeks later the Pentium 120 or 133 Hertz came out right right? If you're younger, think about the phones. 

Speaker 1 

Yeah, yeah, there was a time when every time a new phone come out you wanted it, 'cause it it needed to do this. But I my my phone is. 

Speaker 1 

I think I have the the Samsung 8 right? So that's not hardly cutting edge and I'm OK with that 'cause I don't really feel like dropping $1000 in the new phone even at the rate I cracked the screens, it's just not practical for Maine. 

Speaker 1 

But also my. 

Speaker 1 

My desktop PC's and I still have desktop PC's. 

Speaker 1 

Um? 

Speaker 1 

You know, I. 

Speaker 1 

Haven't bought a desktop PC in at least five years and I'm OK with that. 

Speaker 1 

Because I don't really tax the processor, I mean I do when I do video rendering and stuff like that, but I'm OK with letting it rest overnight. I don't feel the compulsion. 

Speaker 1 

To buy a new computer every 6 to 12 months. 

Speaker 2 

Like I did back in the day, yeah? 

Speaker 1 

You know, and there's no need, so we we are really spoiled and this is, you know, this is right now. I think going to start being an upper speed limit for folks in research, but it's just a matter of time before that kind of reaches off. Kind of in the consumer space and kind of well for data engineers and data scientists going to hit a little sooner than that. 

Speaker 1 

So why is that? Yeah, we've. 

Speaker 2 

Seen this undulation happening, I mean with the first winner we talked about, arguably the second winner that. 

Speaker 2 

Probably ran from sometime in the 80s to eat in the late 2000 parts. I'll say right, you know, 2000 years, but um. 

Speaker 2 

An we anan. Here's the thing. Um, if you do an analysis on those time spans, you'll see that they are, they're shrinking. 

Speaker 2 

Anne Anne, there's every indication that the next, the next, you know, winter, that that we're coming into won't really begin for a few years and that it will also shrink. An it may take here I go Frank, it may shrink in a, you know, in a quantum leap. 

Speaker 1 

Fashion quantum leap forward. 

Speaker 2 

It might, and that's, whereas I think that's where we're going. You know, with the show, here is how does quantum impact you know the the worlds of machine learning and data science and AI? And we both know that it's a. It's a big swap. We also both know because of Q Sharp you mentioned earlier. 

Speaker 2 

But some of the algorithms that have been developed in this tool, that's for all intents and purposes hypothetical. It allows you to code in this language in this, for these you know this hardware that exists. It's not that it doesn't exist today, but it's really rare there's a handful. 

Speaker 2 

Quantum computers on the planet. 

Speaker 2 

As we speak, but the advances that they've made just thinking about. How will we code in it? What? What will Q Sharp our algorithms look like? How will they perform an in doing that? We've already made improvements in, you know, in the performance of these algorithms. 

Speaker 1 

And here's the exciting part. 

Speaker 1 

We are still a number of years away from an actual real quantum computer being practical. 

Speaker 1 

Cost effective and Feasable, Yeah however. 

Speaker 1 

If you look at this chart, um, you can see that. I mean, it's still a number of years away. 

Speaker 1 

Before this is going to be kind of a thing, right? I think you can. You know the next iPhone is not going to be the iPhone Q right? I mean, I still I might be the iPhone 25 or 30 or or whatever. 

Speaker 1 

But the reality here is, and this is the thing that blew my mind. 

Speaker 1 

Is the notion of simulating quantum algorithms on conventional hardware. 

Speaker 2 

Right, right? And that's what's different about this whole thing, I think, right? I've we. You and I both work with simulators. This isn't anything new. We've seen code simulation, but what we haven't seen, I I'll, I'll say it this way. What I have never seen prior to a language like Q Sharp is being able to simulate responses. 

Speaker 2 

On a platform that is being created at the time. 

Speaker 1 

Right, and I think I think it's interesting as kind of like emulation, but I think it's a little more than that. Oh yeah, it's what's interesting is, so here's kind of the thing, and I recently had a discussion about this. I I've so since that kind of that. Ah, ha moment. I've actually may have given 4 customer presentations. 

Speaker 1 

On Azure quantum. 

Speaker 1 

An I've delivered two community presentations on Azure quantum. 

Speaker 1 

Not too bad for a technology that kind of you know doesn't exist. You know? I mean, in Azure Quantum is very real. IBM has an offering. Amazon just announced something Google is working on it. D Wave is a separate company. 

Speaker 1 

There's another company, um, it was very quiet. And then, you know, they all privately fund, and I think they're they might have figured something out. The way they're acting in terms of filing for IPO's and stuff like that so. 

Speaker 1 

A lot of interesting things in this field right are happening right now, but you don't have to wait because here's the thing, simulating. 

Speaker 1 

Quantum computing on standard hardware or even specialized hardware that exists out of regular Silicon, right is here. It's today. Um, there is a YouTube video, um, that as a Fujitsu, has a kind of like a kind of like a GPU, right? I know a lot of people willing to write me and be like that's not really. 

Speaker 1 

Yeah, but in the sense that you can offload some work from the CPU onto GPU like you could with their icy WPF applications or any other thing like that or neural network training. 

Speaker 1 

Um, you could theoretically do that. Now these these machines are not cheap now, but here's the rub. Here's the real business value. This is the thing you want to pay attention to. 

Right? 

Speaker 1 

There are a number of and that's why this fancy slide up, but there's a number of problems. 

Speaker 1 

That can't be solved with conventional compute. It would take the age of the universe or a very long time to solve these problems with conventional compute with conventional algorithms. 

Speaker 1 

Quantum computing, a real quantum computer. 

Speaker 1 

Would be able to do a significantly faster. We're talking things like you know, all the those P equal NP type problems like the really kind of traveling salesperson thing like that sort of stuff. 

Speaker 1 

Is very difficult for classical computing to accomplish. 

Speaker 2 

We have 

Speaker 1 

Quantum it will take you know seconds or minutes, right hours, right kind of what it would, what we're used to now with regular compute, but these are much bigger problems. 

Speaker 1 

Problem is we are some years away from that being affordable. An realistic, right? 

Speaker 1 

Yeah, here's the cool thing. Here's the thing that. 

Speaker 1 

Is not immediately obvious. 

Speaker 1 

What if you were to simulate a quantum computer on conventional hardware? 

Speaker 1 

You don't get the answer in seconds. 

Speaker 1 

But you don't get the answers in hundreds of thousands of years either, right? 

Speaker 1 

It could take 10 months, could take six months, could take a year, but the idea is that it's possible in A and not only in our lifetimes, but in a way that it could actually produce business impact. 

Speaker 2 

Yeah. 

Speaker 1 

That's explosive. 

Speaker 1 

It is in my mind I. 

Speaker 2 

Think because it's very easy to look at that and and just you know, and be Blase, Blase in that kind of response. It's like what you're telling me. This problem is going to take weeks or months to *** and it's like, but you don't understand. 

Speaker 2 

The recursion asked me a few months ago. I would have said this would have taken 10,000 years, right? And and these people don't know it. It's going back to your original class experience, Frank. You know the guy was talking serious. You know AI recursion. 

Speaker 2 

That's a huge part of this. You just you keep Recursing again. The Answers Store it. You get recurse again and get that answer and store it and then you can calculate your probabilities and. 

Speaker 2 

You know that. 

Speaker 2 

That takes forever. If you're doing, you know this process and even with parallel computing, it takes forever. 

Speaker 1 

Right, well, parallel computing I think is kind of bridge. The gap where we don't notice that Moore's law is not as. 

Speaker 1 

Spry and young, as it used to be. 

Speaker 2 

It does help, yes. 

Speaker 1 

It does help. We can kind of ignore it, it's the. 

Speaker 1 

It's the stuff you rub on your joints when they're sore, like it or for the Advil that you gave to make those aches go away. Or you can forget that there's underlying problems. 

Speaker 1 

Right, but I mean that's really the key here and I think you hit the nail on the head when you open up. Q Shock and Q Sharpen is more than just key sharp. Now, as quantum language there's, there's Circ is another language. There's extensions to Python. I forget what it's called, but basically you can write quantum code in Python an ultimately. 

Speaker 1 

It's going to enable. 

Speaker 1 

All sorts of. 

Speaker 1 

New algorithms because you're adding that extra logic gate right that you know. 

Speaker 1 

Because you have this third state. So if you think about the old school ways of logic gates and this is stuff that realistically. 

Speaker 1 

I haven't seen this since I was taking compsci in University, right? I mean, this is not. This is probably not taught in a lot of. 

Speaker 1 

I mean the logic. 

Speaker 1 

Tables are probably taught in these kind of these learning code boot camps and stuff, but the actual how that happens and is manifested in Electrons and surprises. Probably not, because again, we we have the luxury of being completely oblivious to this sort of thing. see I built these circuits Frank. I mean have we write read board putting transistors. 

Speaker 1 

There's a reason why you're here, man. 

Speaker 2 

Back in the day, we used to carve our own chips out of wood. 

Speaker 1 

Out of wood. 

Speaker 1 

Kidding, kidding. But we so. I mean, I would say I would say what's fascinating about this is if you look at this, you know. 

Speaker 1 

This is going to change. Happens are done at this level of detail because ultimately with conventional circuits. 

Speaker 1 

You were dealing with the power of electron. Electron flow is not is flat, right? It's binary right on off 0.0 quantum. You're taking advantage of some of the effects of quantum physics here, and this is what makes it hard. This is what also makes it exciting is that you add the ability of uncertainty, right? Is it? 

Speaker 1 

Is is the. 

Speaker 1 

State going to be represented as one. 

Speaker 1 

4 zero or could it be both right and in future shows? Will kind of explain how can. How does 1 + 0 equal something other than one right? We will explain that, but here's here's a hint. If you're impatient, you're not adding integers, you're adding vectors from right? 

Speaker 1 

I explained that to a guy I work with who was a long history in in designing circuits and servers and stuff like that and I could see like that was the explanation that. 

Speaker 1 

Got to him, he was like Ah Ha and for me that's how I had my ah ha moment too. 

Speaker 1 

Yep, but because you're changing these logic gates. 

Speaker 1 

You have entirely new operators and stuff. 

Speaker 1 

So, um, there is a hot amard gate, right? 

Speaker 1 

I saw the the first key sharp code I saw. 

Speaker 1 

Was basically it was it was. 

Speaker 1 

Dealing with all these different operations and stuff, and I'm like. 

Speaker 1 

That doesn't make any sense. 

Speaker 1 

So as a software engineer or anyone who wants to write these algorithms, you're going to have to kind of Relearn. 

Speaker 1 

Kind of the core fundamental stuff. 

Speaker 1 

And that's a good time to plant the trees. You know, 10 years ago. 

Speaker 2 

Absolutely, and I'll just throw throw into this that I I love metrics math I always have. I just been drawn to it since I way before I had it. You know we're talking. I don't know. Frank, 45 years ago or something. 

Speaker 2 

Or had any use for it at all? But I'm kind of a I'm I'm not super smart when it comes to math, but I'm definitely attracted to to some of these things so you know vectors and set math. I was always kind of attracted to that matrix math and it's a a great time I think to to do A to point out that. 

Speaker 2 

Thanks, gotta throw back to matrix math. 

Speaker 2 

Behind him. 

Speaker 1 

That's that's why I have it on. 

Speaker 1 

If you're watching a video, I have a I have a six monitor setup. It's where I do all my video editing and stuff like that and. 

Speaker 1 

Yeah, I have the The Matrix Screensaver 'cause it looks like I'm in the bridge of the Nebuchadnezzar is as I've been told but but I mean that's really the key, is it? This is the time to learn. 

Speaker 1 

Are you going to be writing quantum? You know, production key, sharp code tomorrow, probably not next week. Also, probably not, but this is going to be a thing and I I predict that you know, in five years time you're going to have recruiters saying, hey, do you have 10 years experience in Q sharp? 

Speaker 1 

That's funny. So even though the language is media two years old, you could it have five years experience in five years of. 

Speaker 1 

Q Sharp true. 

Speaker 1 

Maybe not the 10 or the 20, but I see a similar curve. I see a pattern reoccuring and that's what data scientists do. We find patterns. 

Speaker 1 

And. 

Speaker 1 

So in future episodes, we'll be getting more. 

Speaker 1 

Into kind of, you know what does it mean? 

Speaker 1 

If you have. 

Speaker 1 

Multiple states at once, right? What does superposition mean? 

Speaker 1 

What can you do in the mean time? Um, I am. I am continually learning and reading. I have this book here. Quantum computing in applied approach. 

Speaker 1 

I've written a couple of things based on my understanding and on LinkedIn articles. I'm going to blog a lot about this. I'm going to tweet a lot about this. 

Speaker 1 

But, um, you know the best way to get information out. I think Andy and I have seen is through a podcast. Yeah, now we are lucky to start this podcast. Also as a video feed too, so it'll be sometimes. 

Speaker 1 

You just have to watch the video by my best to explain things. And if you're listening to this only on the podcast feed, but there are going to be some concepts that are going to be hard to get your head around, we'll do screenshots and put him out or something. 

Speaker 1 

We are experimenting. 

Speaker 1 

And the idea for this show. 

Speaker 1 

Only came about at the on July 31st. 

Speaker 1 

Of this year. 

Speaker 1 

So if you're familiar with the data driven, it took us about what would you say Andy about four or five months? Easy, yeah, it was my little for shell, from from ideations to execution. This time I'm doing it in just under 2 weeks. 

Speaker 2 

It's because you rock, Frank. 

Speaker 1 

Thank you, thank you. This is not the first rodeo, so that definitely helps to go a lot faster. Sure. So with that one of the problems we we, we know we're going to have with the Shell and. 

Speaker 1 

Andy's designed electronic circuits. 

Speaker 1 

I know my way around these logic gates, but I'm not a quantum physicist. I never even played one on TV, so I can't go anywhere in a quarantine, so I can't even stay in a Holiday Inn Express. Andy, it's not bad. 

Speaker 1 

So we're going to need help. So if you have experience with quantum physics and you can help us explain these topics or audiences as primarily software engineers, developers. 

Speaker 1 

Data people data people. Of course I love my data P I'm a data people now you are. 

Speaker 1 

So it's definitely gonna. It's definitely going to help if you anyone with experience in matrix math or linear algebra. Well, true story actually. The second time I presented this to a customer Azure Quantum to a customer, somebody in the audience was also fascinated with quantum computing and he actually has a degree in econometrics. 

Speaker 1 

Which essentially is linear algebra applied to economics? 

Speaker 1 

So he I told him, like I mean this, this could be your next career if you wanted it to be because you you have the math already. 

Speaker 1 

Like an you can code. 

Speaker 1 

I mean, if you think back to the the three circle 3, the Venn Diagram of the three circles of you know what made a data scientist data scientist in the early 20 pens they were. They had the code, they had the math and they had the subject matter expertise. He already has two in this field and I hear he's a subject matter expert on some things, but I'm just saying like. 

Speaker 1 

Anyone who kind of knows linear algebra or matrix math, depending on what you want to call it, has. 

Speaker 1 

Are significant bumpiness and this. This is going to do for? 

Speaker 1 

For folks who know. 

Speaker 1 

That level of mathematics and quantum physics. 

Speaker 1 

Particle physics is going to do for them what data science and AI did for statisticians. Suddenly they were rock stars. 

Speaker 1 

I think you're going to see that here. 

Speaker 2 

Totally agree it's. 

Speaker 2 

Gonna be fun and it's definitely exciting and new. 

Speaker 2 

I, as some of our listeners are pointing out here, while we're live only. 

Speaker 2 

Yeah, yeah, they're they're excited about it as well. 

Um? 

Speaker 2 

You and I've lived through a number of these francky. 

Speaker 2 

And I don't know what the right term is for them, but they're definitely these. You know, these points in these peaks. 

Speaker 1 

Inflection point is that the word. 

Speaker 2 

Before that, so yes, inflection points in just technology in general, and I see him about once a decade. I'm just kind of throwing that out there, and it's always something new, right? It's never. 

Speaker 2 

Um, for Maine. I wasn't in the at least I wasn't deep enough into technology in the 80s to see the, you know, the AI bump that happened there, but um, I was around for the web, you know? And uh, you know business. Well then I was around for micro services. 

Speaker 2 

And the cloud. 

Speaker 2 

And this, I think is the next big thing. I think quantum is going to impact everything and it it because. 

Speaker 2 

It touches because it's definitely infrastructure, right? We're talking about hardware. 

Speaker 2 

And because the coding principles are there as well, it touches both hardware and software. And you know, I can't remember. 

Speaker 2 

I can't remember this happening or you know at this level, at the scale at the same time, I just I think this is gonna be the biggest one in my 45 years of hobbyist computer person and then professional. 

Speaker 1 

Yeah, and everything. We've everything we know. 

Speaker 1 

Everything we know about computing has the assumption that it's binary. 

Speaker 1 

It's everything we've run right. Whether you're doing your math homework. 

Speaker 1 

Talking to Siri or Cortana or Alexa, whether you're. 

Speaker 1 

Arguing with strangers on the Internet. 

Speaker 1 

All digital right? It's all that digital state, and it's always been on electrons, right? It's called electronics. 

Speaker 1 

Um, you know, we we have really taken. If you kind of if you I love history so if you kind of take this back further. 

Speaker 1 

Right society there was. We know it was founded after the Agricultural Revolution where we could have a stable supply of food and could stay in one place, right? You don't build cities if you have to forage in the forest or across the Plains every every couple of days, right? Don't exactly. 

Speaker 1 

So our first thing we kind of harness the power of the seed. 

Speaker 1 

Then 

Speaker 1 

During the. 

Speaker 1 

The Industrial Revolution we harnessed originally the power of steam. 

Speaker 1 

And the power of kind of burning fossil fuels, if you will, right? Or the power of fossil fuels or hydrocarbons? Then we got into the ability of harnessing the electron. 

Speaker 1 

Right? 

Speaker 1 

In the middle of last. 

Speaker 1 

Century we got into, you know, the atomic age right where we're harnessing the power of atoms. 

Speaker 1 

I think the 21st century is largely going to be defined. 

Speaker 1 

And by how do we can't harness? 

Speaker 1 

Quantum quantum particles. 

Speaker 1 

Yeah, I, I think that's kind of the next wave. 

Speaker 1 

And and you know, if you. 

Speaker 1 

There's not a lot of other podcasts about quantum computing right now. I think that's going to change. Yeah, part of the reason I want to execute in two weeks rather than five months. 

Speaker 1 

But if you do a search now on. 

Speaker 1 

You get some interesting results if you just do a search on a podcast directory on quantum, right. It tends to be more metaphysical than than about either. 

Speaker 1 

Then about kind of quantum computing, certainly where science of quantum mechanics there's a few. There's a few out just a few. I'm not saying where the first, you know, right? Yeah, but I'm just saying like. 

Speaker 1 

You know, that's kind of where we are. I mean, the point in trying to make is that whenever a new. 

Speaker 1 

Technology or class of technology comes out. 

Speaker 1 

There's always a lot of science fiction and kind of hype around that technology, and that's good because that inspires the next generation of Engineers right totally, but you know, if you think about the 1st. 

Speaker 1 

Arguably the 1st science fiction series was about Frankenstein. 

Speaker 1 

And it was Mary Shelley. 

Speaker 1 

It electricity was the Magic factor that made the dead come to life, right? Right, you see. 

Speaker 1 

Throughout history, if you look at old like sci-fi films in the 50s, right, it was atomic. This atomic that, right? So you're going to start. I think, as this kind of starts disseminating throughout the the the the world you're going to see quantum kind of pop up and and maybe science fiction things. 

Speaker 2 

Yeah, absolutely the we have a couple of questions for myself. I can I do I I so one is. 

Speaker 2 

And I, I think you've been answering this one throughout the show is how is the butterfly effect an exception in quantum mechanics? 

Speaker 1 

So. 

Speaker 1 

I'm not qualified enough yet to to answer that question that would the butterfly effect is, I thought, a proving of mechanics. I didn't think it was an exception, it was the idea that. 

Speaker 1 

A butterfly can flap its wings in Singapore and cause a tornado. 

Speaker 1 

In Kansas or something. 

Speaker 2 

Like that yeah, yeah. And I totally agree with with you on that I. 

Speaker 2 

You know, I. 

Speaker 2 

I I think it's early. I think it's early to you know to even answer that question right, but. 

Speaker 2 

You know it's. 

Speaker 1 

More a physicist or you know, one. We're happy to have him on the show, will record audio or video. We just wanted to do a video for this one. I see the other question is from Yasmin. Is this a series? Yes it is. It is a new podcast. We're streaming this live to do the recording. 

Speaker 1 

Um, because we want to be innovative, but also it actually makes production faster. Believe it or not, like once. Once you go through the trouble of doing it by video the end the post production turn into a podcast is a lot easier than you would think. 

Speaker 1 

Well, it's easy. 

Speaker 2 

It was easier for us earlier than some because Frank built a suite of awesome tools that he's had up. Then it was into Azure VM. SI think to start with. I'm not. I don't know if it's still there or not, it's. 

Speaker 1 

Web service now. 

Speaker 2 

Yeah, like three years we've had. I say we Frank. 

Speaker 2 

And they said he'll just drop the video in there and it separates out the audio for us. And I think it was actually sending it over to the site and um, getting everything ready and data driven dot TV. Um, recently you added um automation around transcribing, yes? 

Speaker 2 

Well, that's still semi-automatic, but yeah semi automatic. OK but yeah, but we've had Frank would just drop a URL in the bucket is kind of where we went to a URL into the text text box and push the button and a few minutes later we have audio sitting over at Datadriven.tv Magic Magic for me. 

Speaker 1 

Yeah, I'm proud of my my tools and. 

Speaker 1 

They're they're kind of they're Speaking of Frankenstein. They're like they're all like this Frankenstein mix of kind of like off the shelf services and stuff I've done so. 

Speaker 2 

I called that automatically. 

Speaker 2 

I called that all day, man. 

Speaker 1 

That's for pro biotic process automation. 

Speaker 1 

There we go. 

Speaker 1 

So I think we've kind of covered thing that I think is reasonable in terms of the first episode. We are going to have more of these episodes. We are going to be kind of limited in the sense of having guests. 

Speaker 1 

Uh because there's not a lot of quantum computing. Experts in the world right now technically all the opportunity. That's most of the opportunity gives us a chance to grow. It gives us a chance to kind of take the the input here and kind of digest it and expose it to really kind of A. 

Speaker 1 

You know a trailblazer in that state, and that's ah, it's not going to be easy, but it's gonna be fun. 

Speaker 2 

Totally agree Frank. I'm glad to be along for the ride. 

Speaker 2 

We're kind of debating it may actually turn into its own podcast. We'll see if we get enough. 

Speaker 1 

Boy right, a lot of that would depend on how much is it gonna cost to get another hosting. 

Speaker 2 

Well, it could still be technically another. 

Speaker 1 

Well, what what? What I can do without it at additional cost was. 

Speaker 1 

Um is have another RSS feed an have a website. Have a web page that will live off the data driven thing, so this is going to be kind of a. 

Speaker 1 

Yeah, 'cause spinoff series like the space nine was to the next generation. 

Speaker 1 

There we go. 

Speaker 2 

Which that worked out really well, yeah? 

Speaker 1 

For so good you know we have the the cool sounding dubstep intro music and stuff like that. Love it. 

Speaker 1 

Now have the AI ability to use an AI to generate the custom readings and stuff like that, and we're always looking for sponsors because we have bigger and grander plans, so our mission is to help people grasp. 

Speaker 1 

Um, these different problems that you know when I first got into data science. 

Speaker 1 

There was definitely like this. This clash of kind of the people who had been in. 

Speaker 1 

In academia. 

Speaker 1 

Assuming that in order to data science data science you had to be one of those. 

Speaker 1 

Researchers, right there was a clash and you know, I think we're going to see the same thing here, right? Yeah, most people were not interested in in business about quantum computing. 

Speaker 1 

Up until you know maybe five or six companies a few years ago, but you're going to see this kind of explode, so you're going to see a lot of these folks who traditionally been in academia kind of thrown into roles of, you know, in in kind of the business world, and they're going to have to deal with the practitioners as they, as they called me in the early days, yeah? 

Speaker 1 

One gentleman in a tweed suit. And this was July 2 in DC. So if you're wearing tweed in July and 

Speaker 1 

See I mean. 

Speaker 1 

You committed your like the mandalorian like this is the way like you don't take off that tweet even in the heat. 

Speaker 1 

He was sipping tea in a very posh kind of Britishness. I'm so glad we now have practitioners like you at these events. 

Speaker 1 

I was like. 

Speaker 1 

Yeah, tell if I was being insulted just now, yeah. 

Speaker 2 

Well, I'll tell you. I remember going through that. Yeah you, you, you and I both go through it you more than me because I never made a claim to data science. I always stuck with you know data engineering and I'm comfortable there. I I'm glad for folks like you and the data scientists that we've interviewed on the show. 

Speaker 2 

And you know. And now analytics, print practitioners, analysis, business analysis pros. But one of the things that happened is there were two or three guests early on the show. Data driven showed that you posed that question to and no one could question their credentials to state of scientists. 

Speaker 1 

That's right, an and did not have PhDs. 

Speaker 2 

They they all. I think one of 'em did and we did have some PHD's in there. Yeah Yeah but even then they all responded the same way. No and I think we were, you know we we talk a little bit about how we started data. You know data driven as as a little reactive in that I I'm not so sure. I think we caught it in the middle of its transition. 

Speaker 1 

At least we totally did and I would like to imagine that we had a little bit of help. 

Speaker 1 

In that transition, so we were one of the little ants pushing it a pushing around the, you know, the big leaf that fell off in the Sky. I don't know. Whatever thing is what I want. I think we were definitely part of the site. Geist, if you will, and and I think that. 

Speaker 1 

There is going to be another shift and I I want to be more. I want to. I want to pay it back like you know we had a record of another show where we were announcing this show. 

Speaker 1 

Um, on data driven an. 

Speaker 1 

I want the opportunity to pay it back to because it was paid forward to me. Like you know people. 

Speaker 1 

Said you don't need any changes you can you know re shift your career towards this and I'm telling you the same thing. Yeah now the only thing at this point is I know the quantum computing is going to be a big shift. 

Speaker 1 

For this industry. 

Speaker 1 

But I can't tell you is it going to be in five years or 10 years. I don't right, and I don't know who the winning team is. So Full disclosure. 

Speaker 1 

If you see behind me watching this, that that's my old license plate. When I lived in Virginia. 

Speaker 1 

Right tablet because I was a tablet PC MVP so I was doing handwriting recognition and working on mobile kind of tablet devices five to six years before the iPad came out. 

Speaker 1 

Yeah, right, so I can kind of sometimes get a glimpse of what's next. I don't always necessarily. 

Speaker 1 

Pick the right way Gonna manifest itself so you know, yeah, or how long it will take. I I swore up and down that 2007 would be the year that you know tablets would take off and it wasn't. 

Speaker 1 

But you know what? It did take off I I could be off on this time. I was when it came to the web. I was about on on schedule but that one yeah so. But again, you know. 

Speaker 1 

So little bit of, you know, take that with a grain of salt. Is this going to happen? Yes, it is going to happen in three years. Only you know five years I don't know, but I do think that if you look at the amount of money that's being thrown at this from Microsoft from Google from AWS, from D wave and all these other kind of other firms that are doing this, IBM as well, this is going to happen. 

Speaker 1 

Don't know when. 

Speaker 1 

Yeah, it happened. 

Speaker 2 

And we also know to just by the nature of these things, it is gonna start small. It's small, made an it's gonna grow from here and at some point it's gonna hit one of those inflection points. 

Speaker 1 

That I was searching for that word earlier. 

Speaker 2 

Absolutely, and it's probably gonna hit something. 

Speaker 2 

In it, you know as we go and. 

Speaker 2 

Although Frank and I can't predict when it will happen, we're both in total agreement that it. 

Speaker 1 

Well again, I mean. 

Speaker 1 

More than just these, MIT researchers have started sounding the alarm that we are hitting in a computational limit. 

Speaker 1 

And then that is going to be the next opportunity is solving the. 

Sure. 

Speaker 1 

So cool, so thanks for everyone who were watching this live as you're watching and recording. Thank you. Thank you for the comments and thank you Andy for for catching all those we definitely we have a domain name. It's impact quantum.com the impact of quantum. We're good awesome alright thank you very much and you have a great day. 

Thanks for listening to the very first full episode of impact quantum.