May 28, 2024

Deeptech: The hurdles of making the first Quantum Computer accessible as a B2B Cloud Product - Alexander Keesling, QuEra

Alexander Keesling researched quantum computers at Harvard and, together with a team, decided to found a company based on his research.

Today, QuEra generates tens of millions in revenue, licenses its quantum computer via AWS and is building a new quantum computer in Japan for around 41 million dollars.

In the podcast, we talk about the status quo of quantum computing, when quantum computers will be available to the general public, and also how difficult it is to go from researcher to CEO of a fast-growing startup & where the parallels lie.

What you will learn:

  • What are the challenges of building a great technical team?
  • When will quantum computing become interesting for the general public and in which industries can it be usefully applied?
  • What do you need to set up a company as a researcher and how do you find your first customers?
  • Technical sales: How do you set up a sales organization for a technically sophisticated product?
  • What difficulties do you encounter when you want to export a new quantum technology to Japan?


Alexander Keesling



Unicorn Bakery Whatsapp Broadcast:

Find out everything you need to know as a founder:       

Our WhatsApp broadcast keeps you up to date


(00:00:00) Researcher turning CEO: Job Similarities and differences

(00:07:14) Why is it still complex to deliver quantum computers?

(00:15:24) How would you explain what a quantum computer is?

(00:26:41) What is the ChatGPT-moment of quantum computing - when will it be accessible to the public?

(00:34:47) What circumstances made you build a business out of your research?

(00:45:25) How do you build your sales motion for a complex technical product?

(00:54:41) Why Quantum Computing will change the world and how you can try it today


So our current systems are about the size of one to two
dinner room tables. They're quite large still.
The basic premise is that while all of that can be
simplified and made smaller over time,
the big race right now is to get better capabilities.
And that is what we're providing.
So one of the biggest challenges, since you're asking,
is that we're taking capabilities that are really
just being demonstrated for the first time this year
or just a few months ago and converting that into product.
So we're really reducing that timeline to productize
new developments and we need to do it in a way that
allows the system still to be stable, robust and deployable.
Welcome to a new episode of the Unicorn Bakery.
My name is Fabian Tausch and today we're talking about
quantum computing, how to sell complex products and more.
Alex Kiesling is the founder and CEO of Quera
and Quera is a quantum computing startup,
state-of-the-art quantum computer with 55 qubits
developed at Harvard School as a research project
and then turned into a business.
And Alex was a researcher himself before, turned founder and CEO
and Quera is currently state-of-the-art.
It's possible to book Quera quantum computing capacities
through AWS, which is quite interesting because it's probably
the most available quantum computing product at the moment.
Quera also recently got the confirmation from the Japanese
government to place and deliver a quantum computing system
to Japan for the amount of $41 million approximately,
plus minus, but this shows that in this business
there's a lot of money for the complex projects,
but also we're at the forefront and the beginning of the technology
from a perspective that we don't have the chat GPT moment yet.
So probably you and I, we don't experience quantum computing
that often in our day-to-day life, but it will come at some point.
And Alex predicts it to be probably the end of the decade,
so 2029, 2030.
And therefore here is a first glimpse into quantum computing,
how to build out of university a research project into a company
and also how to sell complex products
and find your product and message market fit.
Here we go with Alex Kiesling from Quera.
Alex, welcome to the Unicorn Bakery.
Thank you and thank you for having me.
I think there are a lot of topics to cover
and the one thing I'd love to start with is your way from a researcher to CEO.
I think it's not like 100% trivial for a lot of people
and probably also not for yourself.
How natural was it for you to turn, I'd say turn the back to the research
and then become CEO of a venture-backed company?
Yeah, I mean it was of course a big step,
but it's not random, right?
It came from having developed the technology as a researcher
that we are now continuing to push forward
and commercialize here at the company.
So I think it is an abrupt transition,
but the people that I started working with when Quera started
are very much the same people that I knew from my days as a researcher.
That's how the whole thing started.
We're working on a completely new technology
that requires very in-depth and specialized knowledge
of tools that so far have been primarily the realm of research.
So coming to the company to lead it
and to transition from an individual contributor
as a researcher to someone that is at the helm of the company,
there's a lot that I had to learn
and I would say there's a lot that I'm still learning
and what was very important and continues to be very important for me
to surround myself with a great team,
some of whom have a similar background to myself
and have been spending years of their lives developing technology and research labs,
but also some of whom are advisors and board members
and all sorts of other positions and functions
that have seen the process of building a company,
of transforming it, of growing it,
of taking technology from its inception all the way to product
and maturing it through that
and having access to that network of advisors, of coworkers,
that is really what makes this possible.
There's a lot to learn,
but I think that will always be true no matter what your background is
and the only way to do it is to form that community that is making it happen
and that is supporting it.
So it's been a wild ride
and it's really possible because of the community.
Which commonalities did you find between being a researcher
and running a company as a CEO?
I think that both in their own way are hard work and long hours
and rely very heavily on community.
I think that for a lot of people there's a conception that researchers are very isolated
and they work as lonely geniuses,
but that's in my experience that is very far from the truth.
There's a lot of science at the end of the day is a human endeavor
and comes from people talking and working together
and having to put away differences to find what the best solution is to move forward
and all of these things I think are the same no matter whether you're doing research in a lab
or whether you are guiding a company.
At the end of the day there's a goal that you're trying to achieve
and you have a team that is supporting that.
In this case I am of course leading this team and setting the priorities
but it all comes from the same kind of very systematic approach to
what is the goal, what are the challenges to get to that goal,
how do we find creative solutions
and I think that's another thing that in research there is a lot of room for creativity
and creativity is exactly what you need when you're being faced with something for the very first time.
Another thing that is important that carries over from research is understanding that
while you might not know the answer, the answer might be out there
and being resourceful and knowing how to find information
and how to reach out to others to seek for that guidance, that is very important.
There is a difference I think between that world of research and being a CEO of a company
and I think it's both difference of scale and difference of time.
Of course the scale at which we do things here is very different than anything I ever did in a lab.
The amount of people that are contributing towards the goal, the coordination that is necessary,
the types of backgrounds that people have and understanding how to create functional teams
that leverage each other's different backgrounds and that can make progress better together.
But it's also a matter of time. In research you of course always want to be moving fast
but the external pressure is often much smaller whereas in a company we are trying to get things out fast
and we have to commit to dates and decisions need to be made very quickly
because again this difference in scale, the amount of people that are relying on this
and that have access to the service that we have, the product and even within the company
this all makes it so that decisions need to be much more timely.
I guess another way of thinking also about this difference in scale is when doing research
while its impact might be broad in the future, the research is really immediately affecting
basically only those that are working on it.
Whereas here at the company we have a product that we have made available to customers
and suddenly the number of people that rely on us executing on our plans is much larger
and they have much less control than other researchers would over the output of what they are getting at.
One question in between because you mentioned there is a team relying on decisions in the business
there are customers relying on or also touched by everything you decide.
What are the current involvements like how many people are working for Quera
how many customers do you have? Just as an overview that somebody who listens
and hears about you the first time understands where you are currently at.
So at the company we have over 60 employees, we've been steadily growing since inception.
In terms of customers we actually have different modes of access.
So our first quantum computer is available on the cloud through a partnership with Amazon
and this gives access to a very large number of users.
So anyone with an Amazon account can go to the Bracket website and start sending jobs to this quantum computer.
So we know that we've received jobs from very, very many different unique customers.
Besides from that large scale cloud access we also engage with customers on a more direct partnership
to help them understand the capabilities of quantum computers
and how to take their use cases and convert them into programs that can be run on the quantum computer.
And for this we've worked with some large companies in finance, in energy.
This is a much more handheld approach and therefore the number of customers that we reach in this way is a lot smaller.
It relies on very deep engagements with our application scientists.
So over the years we've had probably on the ballpark of 20 or so of these engagements.
And recently we've realized that there's an appetite to get access to quantum computing
not just through remote cloud access but also by installing quantum computers in place on third-party premises.
And for this we haven't yet delivered our first systems but we are going to be delivering one to the UK and another one to Japan.
And this is yet another way to reach many more users of quantum computing
because while we are working with the institutions that are purchasing the hardware itself
we are also partnering with them to create successful programs that give access to more users to these systems.
I think just for listeners to get an understanding because it's publicly on your website
for example delivering a system to Japan is approximately 41 million USD in volume
just that people can understand that that's not like, you know, it's an easy thing I think that also takes some time
and we can talk about the complexity of delivering in a second or probably we do it now.
Like what's the complexity currently to deliver a quantum computer to, for example, Japan?
It's a great point. I mean this is cutting-edge technology which, you know, it is a computational tool
but it might not look anything like what you expect the computer to look like, right?
It's not anything like a laptop and the process to build and operate quantum computers is very different.
I can tell you that the way that our first system was built was very much in line with how experimental setups at universities are built.
This is, you know, this required a lot of PhD trained physicists to place optical elements on a table very carefully and fine-tune them by hand
and to operate it similarly it initially took a lot of, you know, active oversight of the system
and over time we've kind of pushed the design and the engineering along
so that now our systems are much more streamlined in how they're built and much more automated
so that the operation is a lot more seamless for users.
So our current systems are about the size of one to two dinner room tables. They're quite large still.
The basic premise is that while all of that can be simplified and made smaller over time,
the big race right now is to get better capabilities and that is what we're providing.
So one of the biggest challenges since you're asking is that we're taking capabilities that are really cutting-edge,
that are just being demonstrated, you know, for the first time this year or just a few months ago and converting that into product.
So we're really reducing that timeline to productize new developments
and we need to do it in a way that allows the system still to be stable, robust and deployable.
So the system that we're going to be delivering to Japan, this is going to happen next year
and there's a lot of work in preparing both the surrounding infrastructure for the quantum computer to be installed
as well as doing all of the testing and validation of the full system here in our offices in Boston
before sending it and then rebuilding it on site.
So it is quite a large effort and this is a process that is being developed today
because again this is cutting-edge technology and no one has done something like this before.
So just to get a super clear understanding, you're building the quantum computer in the lab or in the office
depending how we want to call it but probably somehow both.
And then you're taking it apart, sending it to Japan and then rebuilding it after you know how to put it together?
Yep, that's right.
Yeah, sometimes it's hard to understand how this actually looks like.
As it's not, oh yeah, we know what we do, we just do it a hundred times, here we go.
There's only a very few systems even remotely like this one in the world right now
and most of them have been built over years in university groups.
What would you say how many are currently there?
There's probably about 20 or so across universities that are actually functional
in terms of just the core, the basic core functionality of being able to manipulate individual atoms at the scale that we do.
But fully functioning quantum computers, there's really been only outside of what we're doing here at Quera.
There is a system at Harvard University which is what I developed during my PhD and that has continued to evolve.
That is the most advanced system that has any reported utilization
but what we're going to be delivering to customers is going to be basically that
and it will be the first ever commercial system with that kind of flexibility and performance.
So this actually will be the first of its kind.
I think one question that we have to ask right now because I'm very sure that not everybody knows what a quantum computer is
and I also can't explain it myself, how would you explain what a quantum computer is to a five-year-old?
Yeah, so maybe I'll start with a five-year-old and then move to slightly more complexity.
So I think it helps to start with that comparison and a quantum computer is a computer that has a different set of operations
than what we all know as computers and that means that it is fundamentally different.
Think of the difference between a pocket calculator and the most advanced supercomputer in the world.
They are more like one another than that supercomputer is to a quantum computer
because again the calculator and the supercomputer follow exactly the same set of rules.
One of them is just larger and more complex but the quantum computer has a different set of rules
and while this is a concept that came about in the 80s, it has taken a very, very long time to be able to build actual functioning quantum computers
and we are honestly still in the early stages of what they are and what they will look like.
Now the reason why it's different is because a computer works with bits
and they can be whatever physical representation you want but it's something that it's either a zero or a one
and it's deterministic. The bit is always zero or a one and the way that it affects other bits in a calculation
just depends on whether it is again a zero or a one.
For quantum computers and everything that will come after this is non-intuitive
and this is not because I'm trying to obfuscate but just because it has taken us a very long time to even get our heads around
what quantum mechanics is and quantum mechanics or quantum physics is the basis for quantum computers
but it turns out that we can create quantum bits that are things that are zero or one
or something that is a combination of a zero and a one
and I think that a lot of people's first reaction is to say well okay but that's just an analog system
that has a continuous value between zero and one
and it just gets more complicated than that because when you look at the output of a quantum computer
it is every quantum bit at the output is either a zero or a one
so it's not that you can just treat it as some continuous analog value between zero and one
and these quantum bits can create basic operations between each other
that are just not something that we can describe classically
and there are three important concepts the first one that I already mentioned is this kind of being some combination of zero and one
and this is called superposition
the second concept is that the superposition can be something actually very complex
that we called an entangled superposition
and an entangled superposition which is the core that makes quantum computers really different
is that two qubits or quantum bits can now have correlations that are beyond anything else that we can see with classical
and the way that we first found out about this was through kind of mathematical explorations from Einstein
where he realized that two entangled, in this case we'll call them qubits, separated by many light years of distance
could be made such that observing one and seeing whether it was zero or one
immediately determined the outcome of the other one light years away
without having that light speed time to affect it
so I know it gets a little bit difficult to follow when we talk about quantum computing
but the fundamental thing is that it has different rules
it's an expanded set of rules and it leads to programs where a quantum computer can be exponentially faster or more resource efficient
than classical computers for some problems
and what this means is that if you want to take a perfect quantum computer and represent it using classical computers
today with my laptop I think I can fully predict the outcome of a general purpose quantum computer that has about 20 quantum bits
if I wanted to do the same thing for a quantum computer with about 55 quantum bits or qubits
I would need to use the largest supercomputer in the world
if I wanted to push that to 56 I would need two copies of that massive supercomputer and connect them together
so the scaling here becomes just infeasible
and getting past those numbers with very good quantum computers above 50 some quantum bits
that gives us now access to potentially solving problems that we will never be able to address with classical computers
so there are some problems that we solve inefficiently today
that we know quantum computers will be able to do with fewer resources
there are some problems that today are basically impossible
and for some of those quantum computers will help us get to meaningful solutions
and really think about how many of these impossible problems we can start tackling
and some of the ones that we are very interested in are in for example chemistry
and finding new catalysts or better materials for example for batteries for energy storage
there is a lot of work that we do today that relies heavily on being able to predict the properties of the things that we build and that we create
and that are the underlying workhorse for our industrial society
but I think that having access to large scale quantum computers is going to advance that faster than we have ever imagined so far
quick insertion here
of course I had to ask chat GPT because sometimes it's easy to ask and then hear your answer as well
how would you explain a quantum computer to a 5 year old?
and the answer is quite short
imagine you have a very special kind of toy box
in a regular toy box you can only play with one toy at a time
but in this special toy box you can play with many toys at all at once in many different ways
a quantum computer is like that special toy box
regular computers can only do one thing at a time in a specific order
but quantum computers can do many things at the same time
this makes them super fast at solving really hard puzzles much faster than regular computers
chat GPT is great at what it does
and I can actually tell you a little bit of the subtlety there
and that's that this idea of playing with all of these toys simultaneously
this is what I was describing as superposition
I mentioned that there's three important concepts
superposition which is this doing many things at once
because you're both zero and one
there's entanglement which I mentioned as this unique quantumness of the doing many things at once
and there's another one which is interference
and the reason why interference is important is because this description from chat GPT is great
up to the point of well but then what happens after
because a computer you give it some input and it gives you some output
and you cannot understand this output that is all things at once
so the way that a quantum program works is that we give it a simple input
and then it uses this superposition to create all of the possible approaches at once
but then if we just look at an output we're going to get a random output that picks only one of those
so the importance in building useful quantum algorithms is to use superposition
sorry to use interference between all of these possible programs
so that the ones that are not the ones that lead to the right answer start disappearing
and at the end you end up with terministically a single output
which is the correct solution to the problem that you wanted
so by putting together superposition entanglement and interference
that's how you get a quantum computer to be useful
and to actually give you something out of this you know playing with all toys at the same time
at some point I talked to Markus Flitsch who's building Terra Quantum here from Switzerland I think
and he described for example one use case that I can just easily imagine
if you gave me the task to find the highest mountain in mountaintop in Europe for example
I would have to run around one mountain after another and figure out like oh this one is the highest
no this one is this one is and with a quantum computer you could for example throw a layer completely over Europe
and lower it from as high as possible to figure out
and the first time it touches a mountaintop I think it's the Mont Blanc then in Europe
it touches the Mont Blanc and says hey I saw everything in Europe I covered everything
this has to be the highest position the mountaintop so we can stop the experiment we're already done
because we know there is no higher mountaintop
and this was quite easy for me to at least understand what it looks like in action
yeah I like it
in a very simple example probably
I like it I think that the question is how do you do that without I mean if you're looking for the highest mountain
you're just one person right and you're moving around
if you wanted to have a drape over all of Europe imagine the amount of effort that you would actually have to put into
coordinating all of these things so the key of the quantum computer is that it's actually somewhere in between both
right it's just you but you're somehow able to explore all of these mountains simultaneously
and kind of convince yourself about which one is the tallest in a much faster way
I think it's just helpful to have more and more understanding on how this works
you mentioned we're currently probably at 55 qubits and it needs one absolutely or the strongest supercomputer that we have
56 would need two of those
what is the inflection point where quantum computing becomes more and more accessible in the general public
like I probably you don't like the example I'm not sure but like what is the chat GPT moment of quantum computing
no I actually I love that because I know that somehow everyone knows about chat GPT
and I think that that will happen once we have quantum computers with a few thousand good qubits
and I'm using the word good here in a little bit of a loose way
but the the important underlying concept here is that with with a with a computer like my laptop right I
I don't have to ever think about whether my laptop is doing things right
it just does the the probability that it will introduce an error throughout the computation is very very low
with a quantum computer we can build things with now you know let's just pick a scale about a thousand qubits
but these qubits are noisy and they introduce errors throughout computation
so when I say good qubits what I mean is an effective qubit with with a low enough error rate that it won't actually mess up the computation
and this is a super important concept it's called quantum error correction
that's what the industry really needs to get us to that chat GPT moment
and we're actually starting to see that for the first time now
so there were a few announcements last year culminating with a demonstration led by a group at Harvard
that we were collaborating with to demonstrate that it is possible to build and operate these logical qubits at scale
reaching up to you know about you know implementing complex algorithms with about 48 of these logical qubits
and they need to keep getting better to get us to this this quantum chat GPT moment
but going back to the point of chat GPT I think that there's an analogy that I like
that when I think about how we got to that chat GPT moment
I think that for me as someone that follows only loosely the development of AI
in my life through popular media and so on I've seen three major moments
one of kind of computers doing things that were in the direction of chat GPT
the first one was you know beating humans at chess it was kind of like you know maybe expected
but still a difficult benchmark to meet
the second one was and I don't think that it had many commercial applications
the second one was beating humans at go with AlphaGo
I think that one surprised people a little bit more because of the complexity of that game
and how it actually went about beating humans
and the technology that went into that got a niche market through for example AlphaFold
that is now the best way that we have to estimate how proteins are folding
and how that gives rise to function
the third one chat GPT when somehow suddenly it became a product that is providing now value
to multiple people in very different walks of life
and I think that there's an analogy here with quantum computing in terms of like the hardware capabilities
that first inflection point as I mentioned is something that I think we're seeing right now
the equivalent of you know beating humans at chess which is being able to implement error correction
and make the qubits work better
and as I said this is just happening right now
the next inflection point that I see is when do we have systems that are good enough
at a large enough scale that we're past what classical computers can do
and we can start finding the early applications that have commercial value
and for me this is where we hit about a hundred of these logical qubits
that are good enough to run complex algorithms
and that is because again we're going to be much past the era of what classical computers can predict
and we can start applying it to problems that maybe are very well suited to the type of hardware that we're running
you know things where again like materials problems
where the complexity arises actually because of the quantum mechanical nature of things like atoms and electrons
and that's going to be kind of the similar equivalent to that AlphaGo moment
and that third point of chat GPT I think this is going to happen once we reach a few thousand logical qubits
where we already have a few programs that we know quantum computers of that size could be able to run
that would basically be running in the background and people would have access to them
probably without even knowing because all of these things could be in a data center
you could be connecting to some interface from your phone
and some complex calculation might be being sent to a data center
without you having any idea of what's happening through the internet
yeah fair enough I think it's helpful to get a bit of an understanding
like where we're currently on the trajectory to know and have a few thoughts
and develop the mind for like okay probably at this point in my life I could get there
like just what would you say when do you like from a timeline perspective
knowing that it's a bit of guesswork and not like a thousand percent predictable
like what would you say when is inflection point two and when could be inflection point three
even when it's even more uncertain than inflection point two
oh man I could talk about this for like an hour and a half
so for the next inflection point we actually put out a public facing roadmap
for our technology development over the next few years early this year
and we are actually expecting to get to a hundred logical qubits by the end of 2026
so we're moving very fast there is now a clear line of sight
for how one of our quantum computers is going to have to look like
to be able to run these complex algorithms on a hundred logical qubits
and that is you know in our plans by the end of 2026
now getting to that last inflection point I think that there the answer is still a little bit more open
there's some more work that we need to do on the engineering of the hardware
on developing the right type of applications
the models for integration of classical with quantum hardware
and there's a lot of co-development that needs to happen between the quantum software
the quantum hardware how we implement quantum error correction
I would say that you know we could see these capabilities start to emerge before the end of the decade
as you say predicting the future is always a difficult endeavor
but I think that before the end of the decade we could see our quantum computers with over a thousand logical qubits
Cool, sounds exciting
One question that I had in mind regarding the researcher turning CEO
and also like bringing all the capabilities to life for others as well
what would you say were the circumstances that made it possible to make a business out of a research project
because I know that there are a lot of researchers out there that are thinking
hey why like that are probably thinking yeah I'm not sure if I want to do a business
or thinking hey probably the circumstances are not right
why was it or were there the right circumstances or the right help
for example also from the university to make it possible to build a company out of it
I think that the first somewhat not unique but somewhat rare set of circumstances
is that there was an emerging market for this technology
not because the technology was really developed
but because there was an understanding that it is going to be revolutionary
it really will change how we think about what problems are solvable
and how we think about computation
and we had started seeing some glimpses of how a quantum computer might be built
now this together with also an understanding that one of the applications for quantum computing
might be important for say financial institutions and governments across the world
because turns out that quantum computers might be good for factoring numbers
or will be good for factoring numbers
and that is at the core of how we protect security in many ways today
so both of these things together the kind of the hope of everything that quantum computers will enable
and also the understanding that the technology could have implications for other areas of life like security
brought a lot of interest groups you know investors
companies that want to incorporate quantum computing into their workflows in the future
governments all together to say this is worth looking into investing into
and that's when you saw for example the very large tech giants
start to create their internal groups to work on quantum computing
so timing is really important here
that there was an interest that there was capital put behind it
that there was a shared understanding that the technology is important
and that is the first and most important factor
if you have a great technology that you don't know who's going to benefit from
and no one else sees a potential benefit
it might just be that it's a matter of time until there's a better match that is found
so for us having this emerging quantum industry was very important
besides from that I think being in the right place helps
Boston has a history of innovation and of supporting that innovation
and working with the universities to help us spin out the technology into a company
and working through the Office of Technology Development and others
to set some structure together with being able to tap into a network of
entrepreneurs and investors in the area who are very, very willing to
jump in and help and give us guidance
all of those things were very important
of course it was important that there was a team that was excited and ready to
take the plunge and say we're going to do this and we're going to make it work
and we are going to find a way to take this technology outside of the lab
put it into people's hands and continue maturing it
that is of course very, very important
because as I mentioned at the beginning it's really about community
and the people you work with and the entire supporting structure
What about finding the first customer or customers?
Because I think on the one hand it's a technology as you mentioned
that's currently developing but still in the early stages
not in the early stages of it's getting started at all
but still compared to let's say public perception
so how aware was the customer base already
and how much education is needed to
I'm not even saying convince somebody but more like to create business cases together
What was the state of art when you started with Quera?
Definitely a lot of excitement and intrigue
but it takes a lot of customer education
I think that everyone comes at quantum computing with slightly different ideas
and sometimes the first thing that we need to do is to start having the conversations
of look here's why we think this could be very exciting for you
but also let's talk about what is the current state of the technology
and how as with every other technology
actually more than many other technologies
I think that this is going to be revolutionizing but it's still not magic
it's not that you can just throw any problem at it without thinking about it
so let's work with you to understand what are the kinds of things that keep you up at night
where would you like to find efficiencies
what are things that you would like to do that you have not been able to
and from there spend the time with customers to say great
we understand there's a few types of problems that you're looking at
we think that problem A has the best kind of adaptability for us to turn into a quantum algorithm
so let's work together on that
let's do a quick proof of concept
and then let's work with you to develop internal capabilities at your company
and we'll work together on implementing an algorithm
and also training you to think about quantum computing in a more helpful way
and to continue working with us as we develop the technology
and as we continue to give you visibility into the types of capabilities that are to come
to start expanding the set of use cases that we can test
so it definitely is a lot of education
we're not doing this alone of course as I said there's a growing industry
so there are many other companies that are doing the same as we are
and that's helpful because the technology being still early stage
many customers are looking for exploring different approaches
and comparing the output that they get with different vendors
it also helped us a lot to partner with a very large company with Amazon
to provide access to our system because this gives users both visibility
and an easy way to access it
but that lowers the barrier to entry
because they don't necessarily need to commit to a project together with Quera
they can test something on the cloud and then come back to us
when they need a deeper relationship to help them set things up for success
one thing that we've also seen recently is a growing market for purchasing full systems
not just accessing the computers but purchasing full systems
and one thing that we learned there, it's teaching both ways
teaching customers about the potential benefits of quantum computing
and how we can help them but also learning from the customers about
what is it that really matters to them
and one thing that we have learned is that for the most part
people don't want to buy a quantum computer as a black box that they just get to install
because there's a lot of work still to be done on
who will benefit most from having quantum computers
how are they going to use them
so really the customers want to create partnerships
and to build programs together where we can provide the hardware
and the hardware can be the thing that attracts interest, that attracts users for them
but in a way that we can continue to support that work
so that it is successful, so that customers can benefit from having access to these capabilities
and so that the other side benefits that they are looking for
for example in the case of government funded programs
they're interested in developing a local economy
and having workforce training is important to them
and having Quora partner with them to help bring more knowledge about quantum computing
to train local people to know how to operate quantum computers
to partner with for example algorithm developers that are accessing the resources locally
all of that is coming from a deep partnership between us and our customers
so it's been a lot of work in educating customers
but really more in listening to their needs
and adapting how we go about our business to support those needs
A few questions on that
so the first part, I mean selling very complex products
this always has let's say pros and cons
I would say one of the pros is not everybody can sell it
like not everybody can produce a product
not everybody can deliver even when they say they can
so there is the potential for a huge differentiation
the second thing is it can take some time until you are at a stage where it's so productized
for example the AWS product right now
when I buy Quora through AWS is productized to a scale where I can just start setting it up
comparably to buying a full system
that's it
so how do you get from
I know what I can offer to finding the right message market fit
and actually understanding what at least the most of the customers want to buy
because that's the hardest part
finding a message because you want to scale your product sales at some point
of course with such a technical product
I think that it's an industry that will develop further
I think you're also looking forward to with a more educated market
more use cases are known and more people know why they want the product
but for now as a company it's still like the hard part and the balance between
we know what we can but what is the core offer that we position on the market
because the market already tries to understand
how was your journey to figuring that out
it was a lot of I mean there's no there's no shortcut right
we know what we can do and we know what we will be able to do in the future
and what it takes is talking with a lot of customers
and hearing what they need and then bringing that back
and adapting both the tech development
to get to what customers care about more quickly
to adapt the messaging to come in with the use cases that we prepared
that are more aligned with the particular industry based on what we learned from them
to also leverage the success stories that we already have from working with customers
to be able to say yes we can do this and not only can we but we've already done it before
and here's how it can help you and kind of lowering a little bit the barrier
of how much they need to dig deep to figure out
well is there something that could help us but to really be able to come in prepared
and say here's a proposal of something that we think might be interesting to you
and we can start here and then take it wherever you prefer
so yeah it really is that early stage learning from customers
by just talking to as many as possible and adapting based on what we hear
adapting the tech, adapting the messaging, adapting the customer targets even that we go after
and now that we're entering also this different approach of selling full systems
it's again starting over by understanding as I was saying what is it that really matters to them
and how can we build this to be a story of success for both sides over the long term
so that we can create a user base that continues to grow with us
and that has a way to influence the tech development over the future
and sometimes this means hardware but sometimes this means also the software
and just to give you an example we started out with an open source package called Blockade
which is very good at emulating some of the quantum processing that can happen in a computer
so that it can help users get up to speed with how they would program our first device
the one that is available on the cloud
and it was written in Julia because Julia is a great programming language for efficiency
but we had to adapt from learning that most of our customers had much easier access to programming in Python
so we had to create a new entry point through Python
so it really is that in terms of tech and capabilities
we know where we are, we know where we're doing, we're communicating this to customers
but the much bigger job is to learn from them so that we can find where we can add value
This brings me to the next question, how many of the 60-ish people are in sales?
We have a small sales team, probably about 5-10% of the company
but of course there's the sales team specifically and then there's people that get involved in sales
and at this point this continues to be a large portion of the company
I am still talking with our customers, with potential customers
so many other members of the executive team
we have algorithm developers, we have hardware developers
also jumping on calls with customers, understanding their needs, guiding them through how to use the systems
so that's the full picture
Why I'm asking is because it's such a complex product where I'm thinking about
how important is the actual sales skill compared to the real technical understanding of what the product is capable to do
because I'm currently thinking what is easier to learn, to understand quantum computing
or to learn how to understand the customer needs and sell the quantum computing product
and my personal, very non-educated thought would be
it's easier to learn the sales skills and get better at sales than understanding quantum computing
We have both, we have sales, learning quantum and quantum learning sales
and I think that's really what it takes, that and getting that extra support from technical staff
that their main role is not sales but they are instrumental in driving sales
especially once we started getting towards defining a joint project for example
where the other, you know, our customers might already have a few people in their company
that have a background in quantum or quantum computing or that have been looking into this for some time now
and then it really makes sense to have that interface with technical staff
that can, you know, give them the confidence that we have technology that works
we know what we're doing and that can speak the same language
So to not be reliant on people who know quantum computing inside out and are the perfect sales machines
you find, let's say, a hybrid way of combining and forming alliances, let's say, task forces to
out of people who are sales but also the technical understanding to then position the right, let's say, product
for the problem that the potential customer has so that you find a hybrid version
that makes it easier for all of the parties involved in the sales process to deliver the best results
Okay, it's just interesting because I know, for example, even founders in fintech
currently thinking about, okay, it's such a specific product use case
and thinking about, hey, do I hire people who understand the product better or the sales?
So this is a reoccurring challenge in not any business but in a few businesses
and that's why with such a specific thing it's even better to understand how you do it
because I think that might be a bit different than the typical SAS scale-up that has an in quotation marks easier
sales settle. Yeah, well, I think that this is something that technical sales will always have
some portion of that technical know-how that is required.
So I think I should have asked most of my questions. Do you think we forgot anything we need to talk about?
I don't necessarily think that we forgot anything but I think that sometimes it's helpful to take a step back
from the nitty-gritty and just look at it.
It's such a cool, exciting time for quantum computing, of course for me personally
but I think that we're seeing the birth of a technology and an industry
that I think will have a very big impact in what kinds of things we can do across the world
within our lifetimes and to be able to take technology that seems at first very esoteric
and very research-driven and to be able to put that into people's hands and find the right applications
and have those applications drive also the technology progression.
I think it's super exciting and we were talking about how difficult it is to predict the future
but I think it's difficult to predict it in both ways.
I think that I expect that the progress that we're going to make is going to be kind of exponential
and it's very easy to overestimate in the short term but underestimated in the long term.
So thinking about the kinds of problems that we're seeing with customers that are interested in using quantum computing
I think that that's this first class of problems of things that we're already doing
and we're basically using as a benchmark but there's an entire space of opportunities
that we haven't started even thinking about because we're just not tackling these problems
and I think that predicting how we're going to be using quantum computers in the future
feels to me a little bit like trying to predict the impact of the internet today
but making this prediction in the early 1990s.
So I think it's going to be a very exciting technology to keep track of
and to get involved with for those that are interested in technology
and we bring together expertise from so many different areas.
We were just talking about sales but we have physicists, we have mathematicians, computer scientists,
mechanical engineers, systems engineers, all sorts of different backgrounds
that really come together to make this possible.
I have two last questions. The first is if I'm a founder listening right now
or even a tech person thinking there's this AWS product that I can try out
what would be the easiest applications to test out in quantum computing?
It actually depends on the quantum computer that you're accessing today.
Probably the Quora one when I'm saying AWS Quora.
We have a lot of notebooks for people to get started on the AWS site on Amazon Bracket
and the first kinds of things that you can do are explore some of these underlying principles
of what qubits are and how they work and see what happens if you give an instruction to a single qubit
versus what happens when you give an instruction to two qubits
and how you can start implementing conditional logic at a very, very basic level
and then start expanding from there out to more complex use cases.
And the last question, so I'm as non-technical as you can imagine when it comes to coding, etc.
Is there a way for me to get a glimpse of how quantum computing is currently working
and testing things out or do I have to develop my technical capabilities before?
Well, that's why I was mentioning the notebooks.
Notebooks are very helpful because you don't necessarily need to understand a whole lot.
You can just play around with a notebook and we have some, for example, on optimization
where the use cases can be, you know, let's say that you know nothing about programming
but you know your business in growing, you know, break and mortar story distribution.
And you want to assess within a given geography how do you maximize how many stores you're placing
without putting them too close that they start competing with one another.
And this optimization problem is the kind of thing that on Aquila, our first system,
the one that is available online through Amazon Bracket, you can start playing around.
Just that everybody interested knows how to get into something,
because I know if I understand something a little better, I just have my mind wrapping,
like including these information and some other thoughts,
and then I'll get like a bit more creative on what to do.
So whoever wants to test it out, I'll link to your LinkedIn profile.
That's not for testing it out, but for understanding a bit more because you're posting once in a while.
Then I'll link to Quera, the website, if somebody wants to check this out,
and I'll link to the AWS site because I think it's helpful to understand.
Okay, do we want to use it? Do we want to test it out?
Alex, it's been such a pleasure. Thank you for joining me here at the Unicorn Bakery.
I would offer you the last words of the show to all the founders listening,
and then just a huge thank you from me.
Thank you Fabian, it's been a pleasure and thank you for the opportunity.
In terms of last words, sir founders, keep on doing what you're doing.
We're changing the world little by little, and it can be a daunting task,
but by focusing on creating value for others and building the right teams,
I think that there's so much that we can do.
Thank you Alex. Thank you Fabian.