Good Disruption: Quantum Computing

UVA Darden.

The Disruption, a lively discussion between UVA Darden School of Business professors Yael Grushka Cocaine and Mike Lennox on cutting edge technologies and practices that are challenging the status quo.

Hello Yael.

Hey, hey Mike, how are you?

How are you doing today?

I am, uh, fantastic.

All right, I'm gonna begin with a question, as I often like to do for you, uh, a history question for you.

What was your first computer?

What was the first computer that you owned?

A computing device?

Yes, at, at the household.

Can I go back to my child?

Oh yeah, no, no, that's what I was expecting.

So my dad was always ahead of his time.

Uh, I definitely remember our Commodores.

What was it, 3264, those kinds, yeah, yeah, yeah, that was, those were.

What year would that, would that have been?

I don't know, 80s, 80s.

OK, yeah, so I always say my, my first personal computer was an Apple IIE that my parents got me in high school.

I never had Apple, never.

I was never a Mac or Apple person.

Well, for, for those listeners who are maybe a little younger than us, uh, this was before the Mac had even been introduced.

So this was literally one of the first personal computers, um, but even before that, my cousin had gotten the Atari system.

Oh, we had that, that.

You had that I didn't have that.

I was deprived as gaming systems.

My dad was a really big computer, uh, geek.

He loved that stuff even though he was a chemist, but he used to, uh, buy a lot of equipment very quickly when it first came out.

Yeah, well, I, I mentioned this because if you think about back to those heady days of like the 1970s and 19890s when these first personal computing devices were coming out, in many ways, here we are, many, many years later, nearly 50 years later, and The underlying technology is more or less the same.

You take a bunch of silicon and you have your silicon-based computers here with a whole bunch of bits with the 01 binary decisions that are being made, and we've scaled it to incredible heights, but the fundamental core, you know, physics, if you will, have been the same.

And now we've got something called quantum computing.

Well, it's been around for a while, that true, or I've been hearing the term quantum computing for a while, but I think there's more and more excitement and investment that makes it feel like it's coming.

Well, of course it's, it's not real until we cover it on this podcast.

So, so this is its moment.

We are announcing this thing called quantum computing.

Uh, clearly no one else has talked about this, but, uh, we feel like it's time.

It's time.

I think we've been maybe fearing this topic because this is a tough one.

This is a tough one to get behind the science.

My basic limited understanding here is again, with our traditional silicon technology, 01 binary systems, we now in a quantum world, have the ability to have bits that we call qubits that have multiple states that they can take, not just 01 binary.

Um, so for example, if it could take 8 different states at any one time, and now you multiply the number of these that you have, you are multiplying exponentially in essence, and so these computers have the promise of being.

Much, much more powerful than anything we've seen, uh, to date.

OK, so that's, that's super helpful.

So let me, uh, kind of maybe, uh, rephrase, uh, your insight or kind of cast it in a different, uh, way to think about it for those folks who don't really even understand the silicon, uh, bit part of the things, let alone the cubit part of thing, um, so the fact that we're using certain ways to kind of store and capture the data, what does that affect?

That affects how fast we can process.

Things and actually compute, let's say run through processes or run through queries.

That's my understanding.

Like at the heart of this is kind of computational processing here.

You know, this is Moore's law, right?

Processing power that we could get out of one of these machines.

OK.

And so then somebody might ask us the question, well, those computers that you talked about that we had back in the 80s, in the good old days at home, you know, the, the size has changed.

Like we know that there have been advancements and there.

Has been kind of evolved, you know, the technology has evolved, um, you know, we all know that we're walking around on our phones with the computing power of, you know, several of these old fashioned PCs.

So what have we seen so far that has happened and how is quantum computing going to be different?

Yeah, well, so I think, you know, at one level, this is not a new concept.

So going back to like the 1980s, people had proposed the idea of a quantum computing using these quantum.

Properties at the very micro level here, uh, for this, for this purpose.

Uh, the 1st, 1st commercial quantum computer was introduced in 2011 by D Wave Systems.

So this has entered the marketplace now clearly it has not penetrated it, um, at a high level, but every day it seems like there are now announcements, uh, IBM, others that are kind of making more advances, and, and the way I think about this from, um.

A kind of a technology race, if you will.

If you take a, uh, again, an 8-bit computer in the traditional silicon versus an 8 qubit quantum computer, the 8 cubit quantum computer is, you know, exponentially more powerful.

Where we are today is we can now build chips that have, you know, I don't know what we're at in terms of trillions of different processors in there.

We're not there with the qubits, but the race is this one that's more powerful as it improves and becomes more economical.

In particular, there are points at which you could believe like this is gonna overtake the computational power of the machines that we have today.

Fantastic.

So that helps us understand there's gonna be a limit to how much we can get out of the current way that we can.

Compute and all of the current ways that we build all those CPUs whether they're in our home computers or at our devices, there's gonna be a limit.

We've got smaller, we've gone more powerful, but at some point, whatever this innovation is because of these multiple states that each one bit can take and because of the different states in which and the and the data that we can capture there and the speed in which we can process it, it's gonna quickly overtake it and be able to perform functions that we.

We're not able to perform.

And I think what's gonna be interesting as this plays out is you're going to see it pick off niche applications first, right?

So you're not gonna use a quantum computer to do, let's say like the basic calculations and math that a, uh, a calculator could do because again, the current technology does this incredibly well.

Um, the ones that I've seen people mention that are kind of like maybe the first.

That we'll start to see the use of these computers, uh, cryptology, uh, and in general, uh, you know, something both of us are interested in like highly complex problems, these what we call technical like NP hard, uh, problems.

So, uh, I've seen like the traveling salesperson problem which I all knows very well, but for those of you who've never heard of this, this is just very simply, how do you design the optimal route for a salesperson visiting.

A number of different, you know, homes, let's say if it's a delivery person on Amazon, and it's at one level a fairly simple to describe problem and everyone understands it, but it explodes computationally as you add more nodes as the end, the number of places you're visiting increases here.

So I have another example of a hard problem, which is from my field, how do you plan a project at the minimum duration when you have limited resources?

It's actually really hard to solve, yeah, yeah, so.

I think those types of problems will be the first ones we'll apply quantum computing for.

And then again, if it continues to get more powerful and less costly, you could see it completely overtaking, uh, traditional computing.

So quantum, just the name quantum, you know, like when I hear quantum computing, quantum mechanics, you know, again we date ourselves, but you know, quantum leap comes to mind, you know, traveling and the like, the.

A verse.

So like, so far we've talked about computational speed and capabilities, but like where does the whole like time space continuum kind of come to play?

Is that a part of this equation or not?

OK, this is, this is where we're going to start losing our, you know, track here.

Now a good time to bring it.

Well, let's wait, let's wait one second because I do think some things that I think we, we do understand that we can bring to the discussion, which is.

To be able to work at the quantum level, at this, you know, literally subatomic level here, um, you need a computer that has, uh, some protections, if you will, that we don't have with our current kind of silicon-based computing.

So one sensitivity to noise.

So it needs to be a quiet environment.

Sensitivity, most of our computers are not in quiet environments.

That's right, um, sensitivity, uh, to movements of any type, like just even a micro little movements in like an earthquake, of course, but something smaller.

That's gonna cause problems.

Um, so you really need to have these very, very protected, and my understanding is that's one of the big challenges of making these systems, first of all, operational, and second, uh, economical.

And so what are some examples of places where we might find some of these quantum.

Capabilities.

Well, again, I think some of these bigger companies like IBM and others are, are trying to build these systems.

I, I think about, as we always tend to reference in this podcast, like pop culture references.

So there was a, a great Netflix series called Devs.

Um, I don't know if you watched that one, And it was the heart of it was them building this uber sophisticated quantum computer, uh, and they do a really nice job visualizing how this would work, where it is like a, uh, a room in a building built distinct from everywhere else, and then the computer itself, I think using magnets was like levitated to protect it from variations, and it was a, you know, clean room, but also, you know, uh, you know, sensitive to noise and all of that, that it was handled there.

So I think that's where at least the belief is these systems might need to have some of that.

Interesting.

We're going back to the good old days of a, of a computer room and a and a big server room that we all have to go to to, to conduct our functions.

Now what I'm curious about is, is that what the vision is for a commercially viable system, because that seems like it, that's pretty expensive to build one of these and limiting into what you could do there, um, and so I think that's as, as often it is with technology, like how do we push down the cost curve.

To make these things viable, um, even if we've proven theoretically of how these will, how these will work, and, um, and so that will be really important when we think about this as a disruption.

It's like, is it about to happen this near term, or are we talking about the next 10 years and who exactly are we disrupting?

Um, OK, now I'm gonna bring in, we, we need, we, we've done it, we've gone to the limits of our knowledge on this topic except we even had any.

So, well, I'm so, so excited and honored to, uh, bring in not only, um, professional acquaintance but a friend.

Um, Job Rosenberg, uh, is here to join us today.

He is the founder, um, of many different startups, but in this case general partner and founder of Cubit, um, Qubit Ventures, a venture capital fund that specializes actually in these early stage investments.

In quantum technologies, so just the right person to help us, um, Cubitt is the first fund in Israel dedicated to quantum technologies and one of a few globally, um, so far.

I suspect that that will change very quickly and there will be a lot more activity globally.

Um, Qubit, um, is, uh, a fund launched earlier this year, so it's pretty new.

It has about, or it's raising $100 million to invest in 12 to 15 early stage hardware and software startups in the quantum field, um, from precede to Series A rounds.

Um, Joab, welcome to our podcast.

Thank you very much.

Thanks for having me.

Well, tell us all the things that we got wrong when you walk down the street and people ask you, what is this quantum computing, uh, what is your elevator pitch?

No, I think you are quite right.

Uh, by the way, my, my first computer, I had a Commodore 64 and an Atari, but to be honest, uh, when I was, I think, 13, I still punched cards for an IBM mainframe in uh Bilan University.

Uh, yeah, so I've done that too.

You're clearly more precocious than me if you were at the university at 13, so that's, no, it was kind of thing for this, uh, early high school students.

It was fun.

Uh, so yeah, I think the quantum computers will look a little bit like that, like, as you said, a computer room, but most of them at the moment are connected to the cloud.

There was no cloud back then, so today you can use them over the cloud if you go over to Amazon Brocket, for example, or for IBM.

Uh, quantum computing, you are already able to play, uh, with computers, uh, and pay money.

So the IBM already revealed that they had a cumulative over $1 billion in revenue from the computers that they are putting on the cloud in the last decade. um, yeah, and the other companies, even the private ones who are building computers, Quantinum, for example, they have a revenue of about $50 million ARR.

Um, so this is quite big, and I think there's a big misconception that people don't realize.

People usually imagine it's something that will happen in a decade from now, and up till then it's an open field, uh, but there are companies already making revenue.

Can I, can I ask you a question on that?

I is the future then of this, um, not that, you know, we all go out and buy our quantum computer to have at our desktop here, and rather, like you said, large players building out basically their own data centers, if you will, connected to the cloud, providing services.

Um, so again, we won't, we won't be buying our own quantum computers per se.

Yeah, yeah, I think not in the near future anyway.

Uh there are enterprises and corporates that are buying on, on-prem computers from private and from public companies, so you will see that.

And in that sense, it's again, it's quite similar to the mainframes of the 60s, 70s, and so on.

Um, each of them, if you buy on-prem, would be between 20 to 40 million each.

Um, these are the prices.

Uh, I think IBM already sold, uh, built more than 75, I think they said, so it's quite advanced in terms of Commodore 64, right?

So they're still, they're still small, they're not doing the things that we know to prove they will do when we scale them up.

Uh, but people are buying it mainly to try and get ready because it's not something, as you said, the knowledge gap is quite huge.

You need to train people.

The talent gap is quite big.

So if you want to be ready, and of course the main question that we will have to address is the timeline.

Uh, but if you want to be ready as a corporate now, as a user, uh, you need to start building your own quantum computers, quantum computing team internally.

So either you use it on the cloud or you buy your own, depending, you know, on your secrecy, on your priorities, because all of the cues at the moment of all the different computers that are available are all taken and you pay something about like $1000 to $5000 per hour.

To use the computer over the cloud, so some companies may rather quickly get to the point that they say we want to have one on our premises.

Amazing.

And can you give us an example of, let's say, um, what kind of task would a company go to the Amazon, you know, uh, quantum computer and try and conduct?

Like what, uh, what from your experience and from your knowledge, what kind of tasks are people engaged in, right?

Like, obviously, is it the travel traveling salesman or is it something kind of, uh, totally different that, that we haven't even thought of yet?

Yeah, I, I think it's the hard problems you mentioned earlier.

You can put them into families of problems.

In terms of industries, it's, it's quite rich.

So it starts with the financial world, um, any material sciences, chemistry, drug discovery, mobility, logistics, cryptography, and many, many problems.

In terms of the family of problems, it's what we know to be the difficult problems of computation.

Um, similar to stimulating molecules like big molecules, especially when you don't have a lot of data, so alpha fold is not relevant.

You want to design a very new one. material sciences, fertilizers, batteries, uh, up till now we have to say because of this kind of a mistake that we've done in the 1940s, inventing the 01 kind of computer, whenever we want to do simulations of molecules, we do approximations.

Um, because, you know, the degrees of freedom of a molecule, a big molecule, many atoms, they can move in space, uh, it's impossible to do on a regular computer, high powered computer, or anything else.

So we're doing approximations and therefore, for example, when you do drug discovery, then you have.

A decade of experiments to make sure you're not killing the person, so it takes you $1 billion to introduce a new drug, but if you are able to really solve the physical problem of how the molecule will fold exactly with a quantum computer in the future, you save years and years of introducing. new drugs.

The same goes for climate.

We're not able to do climate calculations on computers as we know them today, but we will be able to do much more on computers, quantum computers, uh, so in terms of climate solution, you know, trying to, uh, put you in the disposition to say it's a good disruption.

Uh, you will be able to do that, uh, but it's really a plethora of different algorithms in many different, if you go again to the IBM website, you'll see endless amount of use cases that people are already playing with in the sense of taking a small model, translating it from the classical computer to the quantum software language, which is not an easy task, and then running it on the small computers that we have available at the moment.

That usually run around 200 cubits, but again for the pedestrian listener, uh, when we say that the quantum computer, let's say grew from the scale of 100 cubits to 200 cubits, it's not 2 times better.

People have to have in their mind 2 100 going to the 2 200.

This is huge numbers.

The, the improvement is, is unbelievable.

It's I, I saw one stat that said that, uh, the Google's uh sycamore.

The system took a problem that took over 10,000 years to solve by supercomputers and got it down to 200 seconds.

Yeah, yeah, that's the kind of number Google likes to put on the PR.

Now on the Willow, they've done even better.

They said septillion years, which is like many times more than the age of the universe, that they could do on the Willow in 2 hours or so, I don't remember, or even less.

But these Problems that are not that practical, so there's a differentiation again between different levels.

There are the mathematical abstract proof that we do on complexity and so on.

So the show algorithm is the best example where we know that we could, for example, break the RSA code which was based on problems that computers, regular computers, could not do.

So we know as a proof that we can do that, we could do that when we scale the computer enough, but that will take longer.

Then there are the practical, uh, actual problems that we want to solve like the traveling salesperson, uh, and then we can say exactly like how much cubits we will need for which kind of problem.

And then I think the most interesting at the moment is like the tinkering.

So, as usually happens with algorithms, you have the software developers or PhDs in algorithms that come up with a new solution, a new way to do that, and At the moment in quantum computers, it's, it's a very new realm, so people can come up with new ideas that are more on the tinkering side, you know, not this septillion compared to 2 hours, but actually I can save you like 40% of the time on option pricing or I can save you 40% of the people that you are now doing in your huge data center and make it much simpler and cheaper.

So these are the kind of things that I think will come earlier and will be more surprising and will may catch many in the industry unprepared if they don't start to use it now.

So there are examples of companies, enterprises that are doing a lot like JPMorgan, BMW, Aramco from Saudi Arabia just purchased a computer, and there are others that are lagging behind and I think not understanding how quickly it may change the industry.

I'm curious.

Oh sorry, I was just gonna ask the, the, um, the programming side.

I'm just a quick deep dive on that.

So, I am old enough to remember like machine language, right?

And like that going back to the real basics, and then we, of course, built a whole infrastructure off of like that base level programming.

So now we can, you know, like average human can can do this here.

You don't have to be that uh steeped in like at that level.

It is a similar thing happening with quantum computing or are we at a point like we're in the machine.

You know, programming level of uh the basic of quantum, yeah, yeah.

Yeah, well, we are reinventing the whole stack from the hardware to the top level of the software and application layer, uh, but you see quite a similar phenomena, so you don't need to really know the assembly of it.

You don't need to know how to control the actual qubits, the physical qubits, because there are many different.

Uh, types of qubits, uh, and then you want to shield it even farther, you know, for people who are from the drug discovery industry or from the financial world, uh, and you want to let them have this like upper level kind of language that allows them to, you know, just translate the regular problems into.

Quantum problems, but in any case, you will need, there's a big Israeli company called Classic who builds software for quantum computers.

I personally believe that you will not be able to really shield it completely away, and someone who doesn't know what a quantum computer is will just run his software over and above it because The principles of it, the, the fact that you get a huge advantage, an exponential advantage, you need to understand what superposition is, what the qubit is, what entanglement is, otherwise you will not be able to take the advantage that you can, uh, but it will still be in the upper level and maybe AI will help you, you know, as, as a as a co-pilot of some sorts.

Yeah, that's super helpful.

Um, so many questions, so fascinating.

So, um, so it really helped, I think, bring to life your comment around the, uh, let's say the pharmaceutical industry as a practical application in terms of like thinking about the disruption side of things.

Uh, so you're, you're, I just wanna kind of reiterate and, and thank you for that example because you're the, the quantum computing is allowing us to do more exact calculations because we don't.

Need to approximate as much because we can process to a much finer uh degree and it's it's even like a mind change around the fact that there are drugs out there that are suitable to solve certain, you know, health situations that our only limitation right now in discovering them has been our ability to exactly define them and study them in that specificity so we've had to experiment with human people.

For like decades, right, like it's, it just blows my mind that that's actually something that we can change, right?

We can get better at it.

And this is, you know, a question for you, Joe, that I'm kind of interested in is, um, I think about something like chess, right?

And so we've had these great advancements in AI, but to be clear, what AI is doing is it's approximating a solution to the chess game in ways that are far more sophisticated than we as humans can do.

But if I understand correctly, quantum might let us actually solve chess, like, actually calculate the optimal path at any point in time in the game.

Um, is, first of all, is that characterization correct?

And if so, what does this mean for AI if we're, we're no longer using the techniques of AI to approximate solutions?

We're just, we're actually just literally solving problems that, that are out there.

I do believe chess is quite a small problem comparable, so chess, I think we computers today can actually solve in the sense of going through different possibilities and putting different weights on different possibilities and therefore you're right to say that they're solving it in a very different way than, uh, you know.

Uh, world champion human being who's playing chess, uh, because he cannot do this calculation, but let's remember, you know, computers have a better memory than humans since the 1940s, so they're getting better and better, but they always used to have capacities that are better than human beings, you know, just calculating 1,206 times, I don't know what, uh, so I think.

But it's completely true that some of the end users, the potential end users, don't even have in their mind the possibility of using such a computer, because let's say Jali is right to say sometimes it's about computing in a more exact way.

But sometimes like in the traveling salesperson, it's about taking many more constraints and many more parameters, more than you ever thought and more than you ever thought that you need, because when taught in the university computer science, they told you this is unsolvable, so don't go there.

So all of the practicality was about.

Can we do things in this area and not in that area because it was completely impossible.

So people need to learn again what are the possibilities, and I think many of the new algorithms must come from there when people realize what's happening.

And of course as computers scale, that's, that's part of the thing that we still need to see coming.

Yeah, and, and related to the scale, Mike mentioned this sensitivity to sound, the sensitivity to movement, um, this fact that you kind of, uh, reminded us to think about it more like an old traditional kind of mainframe, um, so is there a lot of kind of activity that we're seeing, uh, trying to change that, trying to make these quantum computers more robust and more, uh, um, accessible?

Yeah, that, that's the main effort.

Uh, I think the two main problems of quantum computing, so one is scaling and protecting against, against noise, and the second is the talent gap.

How do you get people, because it's much, much more difficult than AI or blockchain or any other revolution that we've seen to teach quantum mechanics.

Uh, that's a different story.

Um, I think.

That in terms of scaling, there's still the competition is still open.

We have, it's unlike the transistors, all of the computers are built out of transistors from all models from all companies for the last, I don't know, 60 years or so.

In quantum computing, we still have different modalities for the physical qubits, so the hardware is very different.

We have at least let's say 5 leading modalities and there are some more that are more secondary to them, so IBM, Google, and the others are doing what is called superconducting qubit.

So it's based on superconductivity and therefore it needs to be in a fridge and cooled down to millikelvin, um, and that limits the scale of how you can take it up from 200 cubits to 20,000 cubits.

Then there is uh neutral atoms, that's like the latest technology.

It came out of Harvard and MIT and really accelerated fast, so they also have about 1000 cubits by now.

Uh, QEra is one of the companies that Google just invented, invested in together with software.

It was 230 million round.

Then there are trapped ions like continuum that is about to IPO hopefully this year.

They raised from JPMorgan in 2024 300 million at a valuation of 6 billion and doing a roadshow of over 10 billion.

And then there's the photonics, the ability to build computers and qubits out of photons, the particles of light.

Uh, and the most famous company there is Py Quantum.

Uh, one of the founders is the grandchild of Schrodinger, uh, and they raised 1.3 billion and still raising, uh, the valuation of again over 7 billion, uh, and they're building a huge facility both in Chicago and in Australia, uh, that will be the computer, so there it's not even a computer room, it's a computer, I don't know, football field or 3 football, uh, that big.

That that strikes me putting on my kind of competitive strategy hat, but like it sounds like we're still in the period of before the dominant design has emerged, which is always an interesting time kind of.

Yeah, right, right, this period where there's multiple approaches, you know, not always guaranteed that one will win, but likely there'll be some winners and losers then on the, the underlying architecture here.

And the trade-offs are usually about how do you prevent noise.

That's, that's the big game.

So you need to prevent it on the physical level in terms of whether using atoms or photons or Josephson junctions of superconducting qubits, and then there's a layer of error correction like you have in classical computers, but error correction on quantum is much, much more difficult to the point that people thought, I think at least until 20 years ago that it will not even be possible, and for that you need.

To go deeper into what quantum mechanics is and how strange it is because when you measure it as you know you change the system, so how can you find out if you have a correction because if you want to check if you have a a mistake you're already measuring it, so you're doing something to the system so it seems logically impossible and then some very clever people.

Uh, managed to prove that there is a way to do error correction.

That's part of what Google actually demonstrated last December.

That was like there was a big PR it is usually from the West Coast, so it was nice on the one end, the septillion years to compare, but the real thing that they proved is uh about error correction and the ability to actually implement it.

You, you mentioned before and we purposefully avoided, uh, entanglement.

Yes, I can you, yeah, so could you just explain for our listeners here what that basically means from the quantum world, but then how is that relevant to the quantum computing, uh, application?

Um, I did both physics and philosophy of physics, so I'll try not to go too much into the philosophy of it.

When, when I graduated in 1990 or so and I said I'm interested in entanglement, the physicist told me this is not physics, let alone technology.

This is philosophy.

Go and study philosophy, and 2030 years later, I'm in a lab in the Weizmann Institute, or you could do that on your laptop and you actually play with entangled pairs.

Uh, that's quite astonishing to me.

So there are 2, actually, maybe 2 or 3.

Basic principles of quantum mechanics that people should understand about the qubit.

First is superposition, that's the ability of an atom or a photon to be in two states together, similar to the Schrodinger cat that can be both alive and dead.

And again, the main thing to understand, it's not that we do not know whether the cat is alive or dead because we didn't open the box.

We know as physicists we have a proof I cannot do it here.

That the cat is actually both alive and dead, so the atom is actually both in the state of 0 and 1 at the same time, which allows you to compute kind of in parallel many, many different possibilities.

Like if you have, let's say 3 regular bits.

You have eight options, right, of zero and 1, but you need to choose one.

You can, you either choose 0 11 or 000.

You need to put the electricity and the current to go through the transistor.

With a qubit, you can choose to calculate on both 011 and 100 and 111, and it will compute all of them together.

So that's superposition, and that has to do with a single qubit.

Entanglement is a completely different phenomenon that Einstein and Schrodinger came up with in 1935 that actually has to do with two particles, two quantum particles, and that allows you to fully correlate the two particles.

So if you know you have one result at one, you can immediately know what would be the result with the other.

Again, it's different than the classical phenomena of correlation because in classical phenomena, if you let's say mix two coins, what do you say in America, dime and a cent, and you take your hands away and you don't see what you took and you find out one of them was a cent, then you immediately know the other one was a dime.

That's not the correlation of entanglement.

Entanglement is much more weird than that and the fact is that they're both.

In superposition and only when you open one the other one falls back into the other option, so it's kind of a mix like a cubit what I said about superposition and only when you open one, the other one which can be at the end of the universe will collapse into the other option and that's what Einstein called spooky action at a distance.

Uh, and that's, to be honest, a phenomenon that we as physicists still do not fully understand.

And I've heard this argument that you could have like two, I guess, computing systems that are physically separate, but in communication in that way.

Is that the idea here?

Yeah, that's why you sometimes hear also about the protocol in quantum computing called teleportation.

It's not really the beam me up Scotty thing.

It's not this thing, but it's based on the entanglement and the weird phenomena of entanglement.

Uh, by the way, it's a very interesting historical story because there's a book called How Hippies Saved Physics, uh, and it's about physicists in the 1960s, 1970s who were hippies, mostly Berkeley on the West Side, but also others.

They were very interested in entanglement.

It started around 1964, um, and they tried to really dope physics experiments on telepathy.

And tell and all these kind of things, the answers and things, they didn't succeed, but out of that effort and out of that computations and theory came out the quantum computer.

I love fascinated.

Now, now I'm gonna say wait a minute, all of this innovation, it's not because of, you know, improved capabilities around computers and processing and how we think about all of that.

It's just because now, um, you know, marijuana is legal.

A lot of creativity flowing out of the world.

This has been, this is excellent.

Like I think I now finally understand where the whole Doctor Strange comes into play with the whole teleportation.

So, um, this is very, very helpful.

We've learned a lot and I can already see all the exciting, um, activities that you're engaged in, um, as a VC in this field and kind of, uh, both advancing it, uh, from the instruction and the, um, uh, academic side, but also from the.

From the business side, so, um, thank you so much.

Are we ready to make a call?

I think so.

I think so.

I mean, I would observe, you know, yeah, we, we haven't talked in great detail about like the consequences of this, right, um, the disruption, so to speak, the disruption, and, and, uh, we can think about it obviously as we sometimes do from an economic and business standpoint, and I think that's fascinating in itself.

But then the application that Jo mentioned, all the various applications, so my mind is blown and all.

Different ways in which this could disrupt various domains.

Yes, so I'm gonna go back to the pop culture reference, which I think is very fanciful, um, but the Dev show at the heart of it, they use this quantum computer to basically make predictions that are accurate in the sense they will tell you exactly how the future will unfold.

This is your crystal ball, right, so these aren't just approximations like it is incredibly accurate.

And they start and they can only predict like 10 minutes into the future, but then they're able to like get further and further, uh, further out, and you can imagine the implications of that if we know exactly how individual humans and others would behave, brings up huge questions about free will versus determinism, all Minority Reporter all coming to mind.

Yeah, and so I'm, I'm gonna assume that that is the stuff of sci-fi, but let's say we do get.

Better at some of these things, solving some of these complex problems, forecasts that are more accurate and the like.

Is there a downside to that?

Oh, I'm, well, there's a lot of disruption, I'll say.

So like, aside from the obvious, like companies are gonna change, people are gonna lose their jobs because there's gonna be evolution, which always happens with big kind of step changes.

So those could be downsides for sure from a societal perspective.

Uh, the benefits of such a technology could be mind boggling.

All the risk analysis that we do and all the decision making under the healthcare examples you're saying, like, could we solve major, you know, problems and illness and a whole new reality.

I mean, talk about promise, right?

You could cure one of those diseases in some way or learn how to predict better who's gonna get ill and how to treat them.

I, I, I've got a new future topic for us, by the way, because we did this in my, uh, student seminar.

But the curing of aging, which is something that people are really working towards and have said that quantum computing, that's true, that's right, um, if you could stop my aging, I'd be really happy.

But again, this idea of like, could we move to a world of like solving disease and, and some of these, these fundamental problems, um, obviously really, really intriguing.

I, I, from, from maybe an economic but could relate to societal, it is interesting this notion that, um, again, we might not have a quantum computer in our. pocket that we do with computing if it is a handful of companies or entities controlling, you know, these large powerful computers, and that starts to raise some of these questions either about market concentration or just even political power that could, could agglomerate to those, those companies, but I'm not sure it's any different than things we've talked about before with AI and big tech and the and the like as well.

So definitely a disruption.

Good one is that our call here today?

You know, uh, we have our bias in this podcast.

It is called Good Disruption.

Um, I think if we're grasping at straws, I, I think this is a, a powerful technology that has a lot of promise.

So I'm gonna go, I'm gonna go, and I think it is a disruption like that, that it might take a while.

Sounds like we're still early stages from a market penetration standpoint, um, but I'm gonna go with good disruption.

How about you?

Good near future disruption.

Not quite here yet, but probably not that far either.

Well, we don't know, right?

I mean, this is, again, I, I joke with my students that if I could forecast technology, I'd be living on an island that I own somewhere in the Caribbean.

Um, I probably still would be.

I do love it.

I do love it, but, um, you know, and I think this is a technology that's been discussed for a while.

It does seem to be on this improvement, as we heard, lots of investment, lots of revenue being produced.

So like this is.

Real.

This is not like a technology that was fanciful at this point.

Um, how fast that diffusion curve moves, how fast that improvement curves, all, you know, all open questions here.

And I, I would call, uh, the attention of our listeners that I definitely heard, uh, Job talk about like a real talent gap and a knowledge gap.

So it's on us to go and kind of try and challenge ourselves to learn something really, really complicated quantum coder should be.

Uh, studying to be a quantum coder there.

It sounds like it's nice to know that there are sophistication, that there is sophistication out there that Gen AI won't just dumb everything down, but actually we are gonna be have to rise to the occasion and learn something new and challenging.

Now, before we end, I wanna make sure because I said good disruption.

I disruption.

OK, that's what I thought.

I just wanna verify for the record, yeah, you can't avoid the, the discussion there.

Good disruption, and I, you know, I would invest in a job any time.

So, um.

Excellent.

He gets my vote.

Well, we were scared about this topic.

It's a tough one, but I, you know, I learned, we've learned, and that's really what this podcast is all about is whether we learn.

Thank you so much to our guest, uh, and thank you, Mike, for, for this topic.

Yeah, and thank you as always to our producer Gary and to our lead researcher Becky.

Uh, we would not be able to have what even seems like an intelligent conversation maybe to some, uh, without their help.

So, uh, thank you.

Thank you.

Good Disruption is a podcast from the University of Virginia Darden School of Business.

Darden