June 9, 2022 • 50 mins
Estelle Inack is a research scientist at Perimeter Institute, working at the intersection of quantum matter and artificial intelligence as a member of the Perimeter Institute Quantum Intelligence Lab (PIQuIL). She is also the co-founder and Chief Technology Officer of yiyaniQ, a quantum intelligence startup. Her research aims to develop quantum-inspired algorithms to tackle real-world optimization problems using state-of-the-art machine learning techniques. Originally from Cameroon, Inack tells Lauren and Colin about her childhood fascination with naval architecture, and the path she took to pursue a career at the forefront of quantum technology. View the episode transcript here.
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Conversations at the Perimeter is co-hosted by Perimeter Teaching Faculty member Lauren Hayward and journalist-turned-science communicator Colin Hunter. In each episode, they chat with a guest scientist about their research, their motivations, the challenges they encounter, and the drive that keeps them searching for answers.
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Episode Transcript
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(00:00):
(upbeat music)
- Hi, everyone, and thanks
for coming back toConversations at the Perimeter.
Today, we're bringing you aconversation with Estelle Inack.
She's a research scientisthere at Perimeter,
and she's also the Co-Founderand Chief Technology Officer
(00:23):
of the company yiyaniQ.
- I love this conversation with Estelle,
partly because I foundit a little challenging,
the terminology likeartificial intelligence
and machine learning and neural networks.
These are terms that I've comeacross before in our work,
but they get thrown arounda lot in popular culture.
And it was great to hearfrom an expert who's working,
(00:44):
not just in these fields,
but really finding theintersections between these fields.
She was a very generoustour guide with us.
- I agree, and I really also loved hearing
about how her work isreally at the intersection
of quantum science andartificial intelligence,
but also at the intersectionof academic research
and industry applications.
- And her personal storyis pretty amazing too.
(01:06):
You know, she's a scientistwho's now working in a startup.
She's trying to learn the business world.
Estelle just has a fascinatingpersonal story as well.
She's originally from Cameroon,
and she originally wanted to do something
completely different than physics.
We won't give any spoilers,
but her journey into physicswas really fascinating,
especially because she facedquite a lot of obstacles
(01:28):
in her native Africa tobecoming a physicist.
And we learned that she'sactually gone back to Africa
to try to help inspire otherwomen scientists there.
- We're excited for youto hear the conversation.
Let's step inside thePerimeter with Estelle Inack.
(upbeat music)
Okay. Hi, Estelle.
Thanks so much for sittingdown with us today.
(01:49):
- Thanks for the invitation.
- It's great to have you here.
- It's my pleasure.
- So you work in a really exciting field
that's also pretty new orat least rapidly growing,
that is often called quantum intelligence.
Can you tell us a little bit
about what draws you to thisfield and why it's so exciting?
- So it's basically a very fancy name
(02:10):
that means a lot of different things,
depending on how you takedifferent combinations
of artificial intelligenceand quantum computing.
So for example, for some people
it might mean using quantum computers
to perform artificial intelligence tasks,
in the field that they callquantum machine learning.
For other people, it couldmean using quantum computers
(02:32):
with artificial intelligencefor quantum control,
for example,for quantum state preparation.
For other people, it can mean
using machine learning techniques
that you borrow from AI research
to basically probe the behaviorof quantum many-body system.
And this is more of thefield where I am now,
borrowing machine learning techniques
to probe the behavior ofquantum many-body systems.
(02:54):
- You mentioned a few terms there
that I'm hoping youcan elaborate on a bit.
A lot of people have heard theterm artificial intelligence.
It's very much in the news.
I think a lot of people
have heard the term quantum computing,
maybe a little bit less soin the public consciousness.
Can you tell us what those are
and how you're sort ofbridging the two fields?
- Yes, so there are so manydifferent ways, as I mentioned,
(03:14):
and different ways ofbridging the two phase.
So quantum computers, for example,
is just a different way ofcomputing, a different paradigm.
It's using some of theproperties of quantum physics
to hopefully speed up some calculations
that are currently intractable
on the current class ofcomputers that we have.
Some people used to call that
(03:36):
like the second quantum wave revolution,
because already with thecurrent computers that we have,
we already use quantummechanics, transistors.
But now we want to use, to leverage
other properties of quantum system,
either entanglement,superposition, quantum tunneling,
to yeah, have some speedup on some algorithm
like Shor's algorithm, for example.
So it's a totallydifferent kind of paradigm.
(03:58):
Now a artificial intelligenceis in general, basically
thinking about having anintelligence that is not human
that is able to perform human-like tasks.
Right, and under it, you canactually write some algorithms
that we do that.
You have machine learning
and you can have neural networks,
that kind of generally people think of it
(04:20):
like representation of the brain,
even though sometimes it's not like that.
Even though it's remarkable to see
that some of the intuition behind things
like conversational knowledge works is
basically how we see, tobasically come with the design
of that kind of deepneural networks, basically,
to be able to do imagerecognition, for example.
So that's basically twodifferent communities
(04:42):
and a lot of sub-fieldswithin those communities.
And now within the sub-fields,
yeah, you can find some correlations.
I will tell you, for example,one of the correlation
that I'm mostly familiarwith in simulating
quantum many-body systems
on what we call classicalarchitectures like your laptop
or whatever cluster we areusing here at Perimeter, right?
(05:02):
So for us to be able to simulatequantum many-body systems
are different methods.
One of the popular methods isa quantum Monte Carlo method
called variational Monte Carlo.
To use that, you need to be able to have
what is called an ansatz,which is just a good guess
of what the ground state wave function
of your quantum many-body system is.
(05:24):
But to have this good guess,you need to understand
the Hamiltonian or the physicsof the problem at hand.
Is it fermions?
Is it bosons, right?
What are the interaction strength?
What is the Hilbert space?
Is it a Fock space, right?
And based on that, on thesymmetries of the system,
you come up with a good ansatz.
(05:44):
Now, not everybody can do that, right?
We really need very specialized knowledge.
And the moment youperturb the Hamiltonian,
that you go to another Hamiltonian,
maybe it's totally out of your field.
If you leave fermions and go to bosons,
you don't get the intuition anymore.
So the idea of neural networks,
that is borrowing like someknowledge from neural networks.
Since there are universal approximators,
and hopefully they shouldbe able to represent
(06:07):
any kind of function,
then why not representing then
the ground state wave functionof a many-body system.
That was the original idea of borrowing
this kind of neural network,
basically perform quantummany-body simulations.
And even though nowadayswe see that we still need
a little bit of quantum intuitionto make it work perfectly,
(06:27):
like you need knowledge ofsymmetries, for example,
we encode it in a neural network
to make it represent yoursystem in a much better way.
But yeah, so the story is
that, yeah, we saw how itwas working amazingly well
in machine learning.
And it is also startingto work quite well.
- You and I, Estelle, we actually work
in similar research areas.
(06:49):
You kind of said already, we're both part
of this Perimeter InstituteQuantum Intelligence Lab.
We have our matching hoodies today.
- Green hoodies.
That's because the acronymfor that institute is
- Is PIQuILs.- PIQuILs.
- They have to be, everything's green
at the PIQuIL.- PIQuIL.
- Yes.(all laughing)
- And I think somethingthat's pretty unique
about this group, at least compared
(07:09):
to maybe other researchgroups at Perimeter is
that there tends to bea lot of opportunities
for collaborations with industry.
So can you talk a little bit about that?
And what maybe could be unique
or what's important about these academic
and industry collaboration?
- Definitively. What is uniquefirst of all is the field.
The field, as I mentioned,
(07:30):
we are using a lot of state ofmachine learning techniques,
which we know industry use a lot.
Facebook, Google, theyhave huge research groups
that publish a lot of papers.
So already in that sense, we,just by using those tools,
we are already somehow inbetween industry research
and academia research.
(07:51):
- Are those classicalmachine learning techniques?
- Those are classical machine learnings.
Even though now a lotof those big companies
are having quantum groups as well,
and they are developing
quantum machine learningtechniques as well,
and a lot of startups as well.
So the field of quantum computingis being pushed forward,
both by academia and industry.
(08:15):
And the PIQuIL is trying tobridge, I mean, those two worlds
and to provide a platform
where academia can talk toindustry and vice versa,
and together working on the projects
we can speed with which we advance things.
- I often think of it like anarea with many bridges, right?
Because you're trying tobridge academia and industry,
(08:36):
but also quantum with machine learning.
- Exactly.
- Lots of different bridgesyou have to go over.
- Exactly, exactly. Andone interesting thing
that has come up in the last few years
is physicists are thinking
of actually importing some of the methods
that we have been using to quantum matter
(08:57):
to the machine learning community.
I think of tensor networkss, for example.
They're like, oh, we havea very good understanding
on these tensor networks.
We can interpret them
instead of using your black boxes.
So maybe you could use that
for, I don't know, image recognition.
And people have been doingthat and it's working.
So it is also a way forthe physics community
(09:17):
to somehow give back to the AI community.
- You mentioned that whatbrought you to Perimeter
in the first place waslooking at Roger Melko's work.
And now Roger is, he's the head of PIQuIL,
the quantum intelligence lab.
Can you just give us a senseof what it's like at PIQuIL?
What is a day like at the PIQuIL?
(09:38):
What are the sort ofquestions and problems
that are being tackled there?
- PIQuIL is really like astartup like kind of environment.
Even though there'sindustry and academia there,
there's a lot of free discussions.
We have journal clubs.
It was virtual during COVID.
Now we are starting to come back person.
A lot of discussions aboutSlack, "Oh, this is a new paper.
(10:00):
What do you think of?
Oh, I have a problem in my research.
Do you have a solution forthat" and things like that.
So really a lot of interaction.
- And so you first came here to Perimeter
maybe to pursue more theacademic side of things,
but as time has gone on,
you've become more and moreinvolved with industry.
And now you're actually the Co-Founder
and the Chief Technology Officer
(10:20):
of a company called yiyaniQ.
Can you tell us a littlebit about your company
and what it's trying to do?
- Definitely, maybe I will takea step back by a little bit.
I was doing my PhD and thenI was doing my post doc.
So I was mostly focused on academic work.
But even though I was focused on that,
my specialty is developingalgorithms to solve optimization
(10:44):
beside probing the behaviorof quantum many-body system,
but optimization points thatare like real-world problems.
But typically the way we solve it is okay.
Like physicists, we like tohave like a very easy model
that we can benchmarkand things like that.
That is not really reality.
It is not gonna affectthe life of somebody.
And so I always had behind mymind, in the back of my mind
(11:06):
that these algorithms, wecould actually try to use them
to solve real-worldproblems, not just write it
at the end of theconclusions of our papers.
And, oh, you can use itto solve a real-world.
So I had that in back of my mind.
And yeah, so last, Ithink one year and a half,
we had these very nice resultsof an algorithm we designed.
(11:28):
And we decided to basicallyfile a patent away.
And that was the moment I was like, okay,
now we need to try to commercialize it
and see whether we canhave real-world impact.
And we created yiyaniQ.
So the company right nowis focusing on designing
what we call quantum intelligent algorithm
to basically speed up derivative pricing,
(11:49):
which is a specific problemin quantitative finance,
in the sales side of financial market.
In the beginning, Iwas very much confused.
I had a hammer, I didn'tknow where I'd find the nail.
So there are so many differentoptimization point out there.
Some are very interesting.
Some are very challenging.
Others are boring.
I really needed to find onethat was challenging enough,
(12:11):
but I found that very fastthat, yeah, you need somebody
who has expertise tobe able to design that.
And I met him in an incubator
called Creative DisruptionLab, Behnam Javanparast.
And he has a PhD in theoretical physics,
in condensed matter as well.
So we could talk to each other,
but he also worked in a bankfor more than seven years.
So it was quite very easy for us
(12:32):
to kind bring ourperspectives to found yiyaniQ.
- I'm hoping you can tell us a bit more
about optimization problems generally.
Could you tell what the term means
and how you apply your techniques to it?
- Usually for us physicists,
it is useful for us tokind of map a problem
(12:54):
into a configurationthat we understand best.
And one sweet thing is that we can view
optimization problems as a search problem
in a very complex landscape,
where in an optimization problem,
typically you have a functionyou want to minimize.
Everybody more or lessunderstands functions,
but for a physicist,
(13:15):
I can see that function as an Hamiltonian.
Directly when you tell meHamiltonian, I was like, yay.
I have a lot of tools in my toolbox
to be able to deal with that.
And I can view theHamiltonian as a landscape.
You could imagine, forexample, in the Himalayas,
you have a lot of hills and valleys,
can be kind of very crazy landscape.
(13:35):
And solving the optimization problem means
from a physics standpoint is
finding the groundstate of the Hamiltonian
that represents that optimization problem.
But from a graphical point of view,
it means finding the deepestvalley in that mountain.
And for you to find the deepest valley,
you need to search, go up and down.
(13:56):
And depending on how you search,
you can be more efficientin finding the landscape.
But if your landscape, forexample, has a lot of valleys,
a lot of saddle points,
it has tall hills, right,
and maybe very wide hills,
it might be difficult for you
to be able to find the deepest valleys.
(14:17):
This is hardest search problem
where solving on optimizationproblem would be seen.
- Would it be similarto if you wanted to find
the deepest valley in the Himalayas,
you could walk up anddown all of these things,
but optimization is a way,
is an attempt to not put inthat sort of brute force work,
but find the simplest route to the answer.
(14:38):
- Exactly, it's finding thesimplest route to the answer,
which definitely what you justdescribed going up and down
could be mimicked with algorithms.
And it has been mimicked with algorithm.
The most notable oneis simulated annealing,
where going up and down ishaving some thermal energy
to basically overcome barrier
till hopefully, basicallyyou find the deepest minimum.
(15:00):
But imagine that you're goingup and down with your car.
Some moment, I mean, fuel is gone.
What do you do?
So in the simulation is
when you are ramping down the temperature,
and then yeah, there'sno temperature, no fuel,
which means no fuel, no kinetic energy.
And then you get stuck ina local minimum, right?
But you could think ofa different paradigm
which people have thoughtof using quantum computers
(15:21):
or using one property of quantum system
that is called quantum tunneling. Right?
Then instead of goingup and down the valley,
you basically tunnel through the hills
in the search of the deepest minimum.
And then that hopefullywill be a faster way
for you to find the deepest minimum.
This is not a crazy intuition,because when you think
about the way we build tunnels nowadays,
(15:44):
if you're like a buildingcompany and they say, okay,
you need to build like either rail tracks
or you need to build aroad through the mountain,
if you see that the mountainis for example, very tall,
but then the width is not that long,
you're not gonna build thesetracks on top of the mountain.
That doesn't make sense.
You build a tunnel, quantum tunneling.
(16:04):
So that's kind of the idea.
But at the same time, if your mountain,
the height is not that high
but it has a very like long width,
doesn't make sense to build a tunnel.
You just go over it.
So classically it's better.
So that's the reason why most of the time,
people do not carewhether quantum tunneling
or quantum annealing orclassical learning is better.
(16:26):
It totally depends on theshape of the landscape,
and the shape of the landscape depends
on the hardness of the problem.
- You told us that your company, yiyaniQ,
its main focus is using these techniques
on the problem of pricing derivatives.
And that's a financial markets term
that I barely understand.
I believe derivatives are contracts
(16:47):
between financial institutions
that are based on assetswithin these contracts.
That's about all I know,
but it's a difficult problem.
Pricing derivatives, I know,is a very difficult thing.
I'm hoping you can tellus why it's difficult,
how it's currently done
and how you hope to do itbetter and more efficiently.
- Yeah, that's a very, very good question.
Indeed, like we arefocusing on what is called
(17:09):
over-the-counter derivatives
that are mainly traded by verybig financial institutions.
And some of them, they'recalled like structured products,
they are quite complicated to price.
So the way it's currently being done
is using Markov chain Monte Carlo.
And for you to be able to price them,
(17:30):
you need to come up with a large number
of possible financial scenarios
that obeys the law of large numbers.
So the variance of yourestimator, of your price,
goes down with one over the square root
of the number of scenariosthat you can generate.
So basically you need togenerate a lot of scenarios
(17:50):
to come up with an accuracy
that satisfies a trader, for example.
That takes a lot of time.
So we talked to sometraders working at banks.
They told us that some of the books
that have a lot of underlyingproducts in one contract
can take from 60 to 90minutes time to price,
and they need to price it a lot of times
(18:11):
during the day, every day.
So not only it takes a lotof time, since they have,
like they cannot go beyonda certain amount of time,
which means they cannot pricea certain number of scenarios,
they have to reduce thenumber of scenarios of price.
It means they cannot havethe margin that they expect.
So they told us that sometimes
they could be mishedge of $10 million.
(18:33):
That's the error bar
of price.- $10 million error bar.
- Exactly.
- I wish I had that error bar.
- That's very huge.- Well, it depends
which direction it's in.
(all laughing)
- That's very huge.
So idea is basically becausewe know there are some methods
that are more efficient thanMarkov chain Monte Carlo,
be able to price it faster
(18:55):
and also more accurate.
This is what we are hoping to achieve.
So typically you want tofind the deepest valley,
but sometimes it's very hard.
So if you want, you find a valley
that is not so far from thedeepest valley, you're fine.
That's like they call itnear optimal solutions.
That would be fine as well.
Say for example, you're solving
(19:16):
the traveling salesmanpremise, as you mentioned,
if you don't find optimal path, okay,
the salesman will not be angry
if you find a near optimal path.
That saves him time and money.
- Right.- Right?
- He probably won't know
that it's not the actual optimal path.
- (laughs) He probably won't know.
- Yeah, that problem essentially is
how does a traveling salesperson
hit a certain number of cities
(19:37):
in the most efficient way possible.
And it's just a very difficultmathematical problem, right,
an optimization problem.
- Yeah, definitely,definitely, definitely.
So if it's not exactly solved,but approximately solved.
So for the financial case,what we are trying to do,
so the crucial part of our approach is
(19:58):
that we need to be able tomodel the financial problem
of derivative pricing as an optimization.
And then we can use quantum annealers.
We can use all kind of flavorof simulated annealing,
parallel tempering, whatever it is.
We can use variational annealing.
We can use mem, justvariational optimization
(20:21):
with neural network.
So that's where you reallyneed the financial expertise
to be able to cast it asan optimization problem.
That's our approach, which is different
from the approach peoplehave been having before
because we know for example,
there are algorithmson the quantum computer
to solve the price derivative,
(20:43):
like quantum amplitude estimation
on measurement-based quantum computers.
But we could use measurement-basedquantum computer as well,
because we know that the techniques
like QA, quantum approximateoptimization algorithm
can be used to solve an optimization
from a CP-based ormeasurement-based quantum computer.
(21:05):
But by looking at the currentstate of quantum device
with the qubit bonds,with the noise level,
we feel like for relevantreal-world problems,
we are not there.
So our approach was mainly focused
on an annealing-based approach,
plus physics inspired, plusmachine learning techniques.
- And the name of yourcompany is really interesting.
(21:26):
And I'm wondering if you'llshare with us the story
of what the name means andhow you came up with it.
- Definitely, so I like the PIQuIL so much
and the fact that our kind of motto
is kind of quantum intelligence.
So I wanted to have something similar,
but in my local language, to be innovative
and to differentiateself from everybody else.
(21:48):
But I don't speak my locallanguage very well (laughs).
So I kind of, I asked my whole family,
my mom, my dad, my brothersand my uncle and aunts
to come up with a name thatmeans quantum intelligence
in my local tongue called Basaa.
First they told me that quantum,they don't know what it is,
(Lauren laughing)even in English (laughs).
So we kind of put it out of the picture.
I told them, okay, somethinglike shell intelligence,
(22:12):
intelligence of the future,
something like that.
They came up with differentnames and my mom won.
She came with yiyani.
Yi, that means intelligence,and yani tomorrow,
which means the intelligenceof the future basically,
and the Q at the end.
- So intelligence, future, quantum,
it seems like a pretty greatname for what you're doing.
- Yeah, yeah, definitely.- Yeah.
(22:33):
- Do you remember any of thenames that didn't make the cut?
- No, my God, so many.
(all laughing)
- And so I know your companyhas grown a lot, as you alluded
to through this CreativeDestruction Lab program.
Could you tell us a littlebit more about this program?
- Yes, so basically itis like an incubator
(22:55):
for quantum companies.
In fact, they had a number
that about 25% of thequantum computing companies
passed through their program,
can you imagine, in the whole world.
So it's really like oneof the main incubators
of quantum computing companies.
I knew about it before, becauseRoger is very much involved.
I think he's the academic director of CDL.
(23:17):
So I already knew about that.
And when I decided to create a company,
I applied for the boot camp.
So they have a boot campusually during the summer
for about a month half-ish.
And so I went there.
There are a lot of course fundamentals
of quantum computing, quantum physics.
What are the current statesof quantum architectures?
(23:37):
There are so many differentway of building a qubit.
What are the current business cases?
What are the potentialadvantages and things like that.
And then you have a worldcore of quantum enthusiasts.
You could start a company
or you could, becausesome of them are startup,
you could join a company.
I got a lot like offers forexample, during the bootcamp.
(24:00):
But then yeah, so the idea of that is
basically helping people who have ideas
on using quantum computing technology
to solve real-world problems,
to basically groom them, helpthem navigating the landscape.
- And I know you have a lot of experience
working in the academic side.
(24:20):
But probably working in industry,
I guess there's a whole new skill set
that comes with working in this new field.
Were there any lessons
that were particularlyuseful from this camp,
as you tried to build this bridge
between academia and industry?
- Definitively, I stillwant to do research.
For me, the most shocking truth
(24:41):
is that businesses don'tthink like researchers.
I learned that they don'tcare whether you're using
state of art technology or new technology.
They just want you to solve a problem.
And so for me, when I thinkabout, oh, if I, for example,
improve an algorithm of an order
or two order of magnitudes, I'm excited.
(25:02):
If it does not translateinto them earning more money,
they don't care aboutthat, (chuckles) right?
So it makes me have a different approach
on doing research for business.
I have to do research, yes.
I need to think about usingthe best possible tools, yes.
But at the same time,
(25:22):
I need to think aboutpotential business advantage,
which we don't think about.
Of course, we don't think about that.
We are most interested insolving exciting problems.
- It's like optimizinga different function.
- Exactly.
(Estelle and Lauren laughing)
- Was the term boot camp applicable?
Was it pretty intense?
- Oh yeah, it was like, it was crazy.
(25:44):
And in fact, the craziesttime of the boot camp
was it had a two day hackathon.
I think I probably slept like three hours
during those two days.
You had to come up with an idea
to solve a relevant businessproblem using a quantum.
- In two days.- In two days.
- And any problem, or theytold you a certain problem?
- Any problem of your choice.
(26:05):
So they had some problems,that maybe some hints,
but any problem usingsome of the architectures
that were made available to us.
And yeah, program it andcome up with results.
So there was only, notonly the scientific value.
You need to come up with a business pitch,
like do some quick market research,
show that, come up with the numbers
(26:25):
that this is a relevant problem
and have a short videoof making your pitch.
- Hang on, you've got two days
to develop quantum algorithms
and a business pitch and a video.
- Yes.- Okay.
So when did you get thosethree hours of sleep?
(all laughing)
- I was working withBehnam until midnight,
I think.(Colin and Estelle laughing)
- Did you just crash at the end?
- It's when we stopped talking
(26:46):
around maybe midnight or 1:00,
and then I kept working till probably 3:00
and got up at 6:00, andstarted working again.
- And what did youactually end up developing?
- Oh, we basically wrote acode on the D-Wave machine
to solve a portfolio optimization.
And we had to push it on GitHub.
So it's available on CDL GitHub.
(27:07):
- So hold on, not only did you have
to come up with an algorithmand a business plan,
but then you had to push this out
and make it available to other people.
- Yeah, publicly available, yeah.
- And you mentioned D-Wave.
Can you explain a littlebit about what that is?
- Oh yes, so D-Wave is aquantum computing company.
It was the first one toactually commercialize
the quantum computer.
And so they are mostly focused
(27:29):
on annealing-based approach
as is solving optimization.
Even though recently, theyannounced that they are starting
to build also CP-based quantum computers.
So one of the cool thing that they did,
and a lot of quantum computingcompanies are doing now
is if I want to run simulationson a quantum computer,
I don't need to go and buy 10million, whatever the cost is,
(27:51):
- Thank goodness.- And come
and install it at Perimeter.
You can have access to it through cloud.
And so you have an API code
and you just, yeah, pass in parameters
and it spits you backbasically the results.
And you can even see which qubit
you have been using the quantum processor
to basically solve your problem.
- So you can implementyour algorithms on D-Wave,
(28:13):
but in the cloud youcan do it from anywhere?
- Definitely, yeah.
- It's amazing.- Oh, not anywhere.
It depends on where theyhave the clouds deployed.
I think now you can do itin North America and Europe.
South America, I'm not so sure.
Africa, I'm not so sure.
Probably in Japan as well.
So as they are expanding, theyprovide that cloud service.
(28:34):
- And as you've said, Estelle,
it seems like there's justso many different priorities
that you have to balancewhen you're doing this work
at kind of the intersectionof academia and industry.
And we had a grad studentfrom here in Waterloo
send in a question about that.
- This is Matthew Duschenes,
a student at IQC and Perimeter.
I'm wondering, how do you balance
coming up with novel research ideas
(28:55):
versus staying focused on yourspecific startup objectives?
- Nice question, very,very important question.
I ask myself thatquestion every single day.
(Estelle and Lauren laughing)
- Are you able to balance these things
or is it always a juggling act?
- In the beginning, it was so hard.
It was really, really, really very hard.
Now I'm kind of equilibrating roles,
(29:17):
dividing my time half, half,
not every week, but yeah,that's what I'm trying to do.
Because for the company,definitively I'm doing
like an application of my techniques,
but we are in a very fast-paced milieu,
whereby you need to be awareof whatever is state of art.
(29:39):
So you need to be on top of your game
as far as research is concerned.
So I need to keep an open eye
on the research world as well. That's why.
- Must be changing every day.
- Exactly. So it's not as before
that I could read archivepaper every morning.
I cannot do that anymore.
(Estelle and Colin laughing).
I can attend journalclubs, attend conferences,
(29:59):
and I talk to collaborators
to keep in touch with what is happening
as far as research is concerned.
I was groomed as a PhD student
that a problem isinteresting when it's hard.
I mean, if it is not hard,what's the point (laughs)?
So I really like takingon very hard problems
(30:21):
and if they're relevantto an everyday person.
- When you get stuck ona really hard problem,
what do you do to push through it,
to get past that obstacle?
- I do it very badly, usually(laughs), almost depressed.
Anyhow, but yeah, typicallyjust take a step back
(30:41):
and try to do something else.
I mean, go boxing.- Boxing?
- Go swimming or running,something different.
Sometimes involvestalking to collaborators
to get some of the ideas that they have
and coming back to it with fresh eyes.
(31:02):
- So you've been tellingus a lot of stories
of things that have happenedin the last few years.
And I'm wondering if we can maybe go
back a little bit further.
Could you tell us the story
of how you first gotinto being a scientist
or how you first decided topursue that type of path?
- I have a very non-typical path
to becoming a scientist.
(31:23):
Yeah, so right away Ishould make a disclaimer.
I didn't plan to be a physicist (laughs).
- This wasn't the lifelong dream.
- Nope, it wasn't a lifelong one.
Yeah. Well, I wanted todo naval architecture.
So I was advised during my high school
that for me to do naval architecture,
I needed to have a bachelor in physics.
(31:45):
So I got.- Sorry, naval architecture
is designing ships?- Ships.
- Okay.- Yeah.
It's very, very different.
- But maybe you're gonna use your methods
for naval architecture next?
I guess we'll see.
- Yeah, why not?
- Maybe there's an optimization problem
in naval architecture.
- Oh, naval architecture, I don't know,
but definitely in the maritimeindustry on ship route,
(32:06):
there is an opportunity for that.
I even thought about that,
either ship route or ship loading.
For example, imagine I have a big cargo.
It has to load on thousandsof different containers
on the cargo.
What is the best way for you to do that
to optimize the space?
I actually wrote analgorithm that, VNA, yeah.
(32:27):
- Shipbuilding and ship architecture,
where did this come from?
- Since my mom worked inthe maritime industry,
I was very much influenced by her.
So I wanted to do a job
that was related to sea, ocean, right?
But I wanted to do a technical job,
something that I coulduse some of the things
I was interested in, mathematics, physics.
(32:48):
So I found that navalarchitecture was the best,
but I was not well-advised.
I found out that you cannotdo naval architecture
with a bachelor in physics.
Then I wanted to do computer science
after when I found out Icouldn't be a naval architect.
But unfortunately thatyear in my homeschool,
they didn't open up amaster in computer science.
(33:10):
The only available master was in physics.
So I was like, okay, Ineed to go to school.
Let me just really startfor the master in physics.
And I like it.
It was very easy for me to do,
and I think I got first class
and then I got a scholarshipto go to Italy to do it.
- It sounds like you're still interested
in shipping and ships.
Is that an ongoing fascination for you,
(33:31):
the maritime industry?
- No, I think it after I wasso disappointed, I should say.
I was really, really, really disappointed
when I found out I was just missing that,
so that kind of died out.
But for computer science, yeah,
I'm mostly programming now.
Almost all of my day I'm writing code.
(33:53):
I kind of brought together my interest
in computer science andprogramming in my physics job.
- Well, we actually got a question
about how to combine programmingwith research in physics.
So could we play the next question?
- Hi, so I'm Hassan Conser from India,
and my question is alittle more career-related.
(34:15):
How do the fields ofprogramming and physics
mix like simulation machine learning,
and is it necessary to learn programming
when going into field of physics?
- From the first part of your question,
if you think about machinelearning for physics,
you definitely needprogramming for that, right?
But if you have to thinkabout physics in general,
(34:38):
it really depends onwhich field of physics.
There are some fields of physics
where not a heavy amountof programming is needed,
some even none, just need to do
some kind of analytical work.
But when you think aboutthe field of physics,
generally as a rule,
my feeling is that little bit skill
(34:59):
on just knowing how to plotfunctions is important.
Just knowing Python, whichis very easy to learn,
should be sufficient to get by.
But if you want in field ofcomputational physics, yes,
you need to know how toprogram a little bit more.
And nowadays it's really easy.
For example, for machine learning,
there are a lot oflibraries you can just use,
(35:21):
I think about 10 or so,
to write prototype of your model
and to test it very quickly.
You have things like Google Colab.
You can use GPUs tosimulate very fast things
and even get some results.
So I feel like it shouldn't be seen
as a very huge barrier.
Programming is actually very fun.
But my advice is that youshouldn't lose sight of the fact
(35:44):
that at the end of theday you're a physicist.
So you need to sharpenyour physical intuition.
I give you the advice
one of my lecturer gave mewhen I was doing my PhDs.
You first of all need totake your pen and paper
and figure out the physicsbehind the problem.
And once you do that, thenyeah, you can take your computer
(36:05):
and write some code.
- You've used the term physicalintuition a couple of times.
I'm hoping you can explainwhat you mean by that.
- Physical intuitionis based, I would say,
on the understanding on how nature works
and the understanding ofsome physical principle.
(36:25):
Take like the Heisenbergprinciple for quantum mechanics.
If you know exactly theposition of a particle,
you cannot know exactlymomentum of that particle.
So when you think about a problem,
you need to have these kind of things
on the back of your mind,
and that will help you notonly interpret results.
It will help you design models
(36:46):
to maybe benchmark somethingspecific about the model.
It's very, very importantto do top class research.
That's my impression.
- You've attended workshops in Africa
about promoting women in science
and just promotingscience in Africa overall.
Can you tell us why youwanted to attend those
(37:07):
and what you hope peoplegot out of your presentation
or your attendance?
- I always find myselfliving in a superposition
of two almost orthogonal worlds.
Unfortunately we knowthat science in Africa
is a little bit laggingcompared to the West,
(37:28):
but for women it's even worse.
It's like this really was
because a lot of cultural apprehension.
It's changing, it's reallychanging, but still sending women
to do what is called hard skills,
typically people think
that, okay, math, physics,it's just for men,
(37:49):
even when they're tryingto be progressive.
Even here in the West for women,
we see that as you go up the ladder,
we see less and less women.
It's even stronger in Africa
because there's morecommitment that is demanded.
And the role of the woman in family,
(38:09):
it demands a lot of your time.
That makes it very hard foryou to do top class research.
Starting to have these conversations,
one of the feeling that I've been having,
that it has to start firstwith women scientists,
African women scientists, the mindset,
to kind of recalibrate themindset that it is possible
(38:30):
for me to do science.
I don't necessarily need to create.
It is possible, and from there,
like put together policies.
I feel like this is very important.
Also educate our male counterparts,
starting with our families toreally change that mindset.
(38:52):
But me in my career, Ihad a lot of instances
of people telling me that,why are you doing your PhD?
You should be married and having kids
and preparing for your husband, (laughs)
right, this kind of a thing.
But I was educated in thehouse when my mom told me
that as a female, you cando whatever a man can do.
I already had that in my mindset,
but other people, they don'thear that kind of thought.
(39:14):
It can really affect them.
But starting to have this conversation,
we hope to see change.
- Is that what you're tryingto do when you go there?
You're trying to help
with this recalibrationprocess for individuals.
- Even not necessarily when I go there.
Whenever I happen to interact
with female scientists from Africa,
(39:37):
which happened once in a while, yes,
trying to have those conversations,change of the mindset.
It doesn't have to be this way.
- I read that your father,
he'd wanted to get a PhD in physics,
but he didn't 'cause thereare more practical paths.
He chose engineering, I believe?
- Yes, actually it was nottoo much of his willing.
(40:01):
So my father was, he is still very smart.
He was very smart.
So he had a government grantafter his high school degree
to go to France, to basically to study.
He was studying physics.
And then he wanted to do a PhD in physics,
but the government was paying his stipend.
It's like, we don't need physicists.
We need engineers.
For him not to lose his scholarship,
(40:23):
he had to move to engineering.
But then he really encouragedme a lot to do physics.
- So what was his reaction
when you obtained your PhD in physics?
- He was very happy.
In fact, he told a storyduring my PhD party
of the fact that when I was doing
first year bachelor in physics back home.
(40:45):
So I did in high school, French education.
I studied in French.
And naturally I'm like French speaker,
my mother tongue, willing God.
But then I moved in English inthe Western part of Cameroon.
It was very, very hard.
I needed first to understand the English
before understanding the physics.
It was, I had a dictionary all the time
(41:06):
when I was going to the lecture.
So it was really, really bad.
One month after startingmy bachelor in physics,
I passed an engineering concours
in the French side of Cameroon
to become an engineer.
So I called my dad.
I was like "I'm stopping this thing.
It's not going; I need togo and do engineering."
My dad told me that "No,Francophones have been able
(41:28):
to go to that school and graduate.
You're gonna stay there."
I was so mad at my dad.
I was so angry.
But after a couple of months,I picked up the English
and I did very well.
And he told the story during my PhD party
that I hold strong and nowshe's a PhD; she's a doctor.
So that was sweet.
- That's beautiful.
- And Estelle, you've toldus so many nice pieces
(41:50):
of your story starting in childhood.
And I know you kind ofhave alluded to the fact
of how you made the decisionto come here to Perimeter
for a postdoc after your PhD.
But I know that you actuallyhad a lot of options
for what to do after you had a PhD.
And I always look back fondly
when you were making that decision
'cause you and I actuallytalked before you came here.
(42:10):
And so I always like to tell people
I was one of the first tomeet you here at Perimeter.
So could you tell us a little bit more
about how you made that decision?
- Yes, definitely.
So one thing I wanted to make sure is
that people I would be workingwith, especially Roger Melko,
I already knew he is a great scientist,
but I wanted to know that he'sa good person to work with.
(42:32):
So I wrote to you and sent you an email
and you were very nice to havea Skype discussion with me.
And I was just convinced.
There's also Giacomo who sent me an email,
who replied to my email.
He told me that it'samazing to work with Roger.
That convinced me thatPI is a great place,
but at the same time I hadopportunities, one in Alberta,
but they called, canceled it out.
(42:55):
There was one at Microsoft,
which was actually themost interesting one.
I had one also in California,which was kind of interesting
because we have collaborationwith people at NASA,
but then I'd be workingmostly on developing further
the algorithms that Ilearned during my PhD.
At Microsoft, I would'vebeen applying the algorithm
(43:15):
I developed during myPhD on WeWork programs.
That was extremely exciting for me,
but I wouldn't have learned
something really new,
would have been mostly application.
Whereas here at Perimeter,
I would have enlarged my research interest
to include machine learningand neural networks.
So that is basically the reasonwhy I chose to come to PI.
(43:38):
And I don't regret that at all.
- And Estelle, now at Perimeter,
your title is Research Scientist,
but when you first came here,
you had the title ofa post-doctoral fellow
under the name FrancisKofi Allotey Fellowship.
Can you tell us a littlebit about this fellowship
and how it was named?
(43:59):
- So Francis Kofi Allotey,
unfortunately he passedaway about five years ago.
He was really a monumentof an African scientist
who literally inspired andtrained generations of physicists
on the African continent.
So he actually did a graduate degree
(44:21):
at the Imperial College London
under the Nobel Prize Winner, Abdus Salam,
who later on created
this famous, the AbdusSalam International Center
for Theoretical Physics.
And then he did his PhD
at Princeton under Robert Oppenheimer.
- He did his PhD with Robert Oppenheimer.
- Yeah, so he was the first Ghanaian
(44:43):
to do almost everything,
the first Ghanaian to earn aPhD in mathematical physics,
the first full professor
in mathematical physics in Ghana.
And as far as research is concerned,
he is mostly known forthis Allotey formalism,
which is basically a wayto detect soft x-rays
in material like lithium orother alkaline materials.
(45:08):
And yeah, so he kind of, I mean, he has
a single authored paper onthat, which is pretty neat.
And he got, I think, a medal for that.
But beside his researchcontribution he had,
he was member of a lotof international bodies.
He created, was one of the founding member
(45:28):
of the African Physical Society.
He was a board member at ICTP
and a lot of other institutions.
And he did a lot of work as well in Ghana,
like creating institutes
and fostering likescience in the continent.
So it was quite an honor for me
to have a fellowship named after him,
(45:51):
almost more than myshoulders could bear, right?
But it was good also forpeople in the West to see that,
because typically you're not familiar
with that kind of a name.
You think more like Einstein,
Dirac, Schrodinger,
but it's good to see thatwe also have, if you want,
(46:12):
we can groom up top class scientists.
So to how did I come up with the name?
So when I was coming to PI,Neil Turok actually gave me
the choice, interestingly enough,
to choose which name I wanted.
And yeah, I chose him.
- Now, you've kind ofexpanded this set of tools
(46:32):
that you have through your postdoc.
And now you're getting to work
on maybe some of thesereal-world applications.
And I really liked thething you said earlier
about how often in papersacademics are claiming
that their methods could be applicable
to all of these potential,huge, real-world problems,
but maybe people don'talways really put the effort
into solving those.
And it seems like that's reallywhat you're trying to do now
(46:55):
in your work, which is really impressive.
And I'm just curiouswhat other real problems
you have in mind to look at next?
- Oh yeah, maybe not to look at next.
Kind of the biggest problemI have in mind to solve
is protein folding.
- Protein folding?- Yes.
- What is protein folding (chuckles)?
- So basically a proteinto be functioning,
(47:16):
it has to have a certain conformation.
It can take millionsdifferent conformation.
When you're doing protein design,
you need to find aconfiguration white box.
They call it like whenthe protein is native,
in its native state orin its folded state.
And usually it startsfrom an unfolded state.
(47:39):
And the path through the folded state
is like you going through the Himalayas,
is a very, very hard path.- Peaks and valleys?
- Exactly, exactly,
with a lot of like localminimal saddle points.
It's very hard, and notonly is it a hard problem,
it's very relevant, drug design
to help us like have better drugs
(48:00):
and help in the health sector.
So this is also, it'shaving a very strong impact,
but it's also very hard to solve.
So this is one of theproblems that I'm thinking.
I know that the approachwe are having now is
really to develop state ofoptimization algorithms,
that of course, in the company right now
(48:20):
we apply it in the financial domain,
but that we can easilyexport to other domains,
like in the domain of protein folding
where the only subroutine or function
we will have to changeis just the Hamiltonian.
But then we will need tohave domain knowledge.
And how do you write theprotein folding Hamiltonian?
And that actually is nota minimalistic model,
(48:44):
is not an easy model,
but it's really a modelof a real-world model.
I cannot; you reallyneed somebody who works
in either quantum chemistry
or biotechnologicalsector and things to be.
- Just to see if I'm understanding,
right now when you're working,
Behnam, your co-founder
is kind of bringing thisexpertise of the finance side.
(49:06):
So you would kind of need
someone analogous to Behnam for this
protein folding problem.- Exactly, definitely.
So we even talked about that with Behnam,
that once we will be able to create value
with those algorithms, we start exploring
what we call in businessjargon other verticals.
(49:26):
- Well, it seems like there'sa lot of potential options
for the future, and I'mreally excited to see
what you're gonna optimize next.
- Thanks.
- Thanks for chatting with us today.
It was really fun.
- My pleasure.
(upbeat music)
- Thanks so much for steppinginside the Perimeter.
Be sure to subscribe so youdon't miss a conversation.
(49:49):
We've interviewed a lot ofreally brilliant scientists
whose research spans fromthe quantum to the cosmos.
And we can't wait for you to hear more.
And if you like what you hear,
please give us a rating or a review
wherever you get your podcasts.
Great science is for everyone,so help us spread the word.
(upbeat music)
- Hi, everyone, and thanks
for coming back toConversations at the Perimeter.
Today, we're bringing you aconversation with Estelle Inack.
She's a research scientisthere at Perimeter,
and she's also the Co-Founderand Chief Technology Officer
(00:23):
of the company yiyaniQ.
- I love this conversation with Estelle,
partly because I foundit a little challenging,
the terminology likeartificial intelligence
and machine learning and neural networks.
These are terms that I've comeacross before in our work,
but they get thrown arounda lot in popular culture.
And it was great to hearfrom an expert who's working,
(00:44):
not just in these fields,
but really finding theintersections between these fields.
She was a very generoustour guide with us.
- I agree, and I really also loved hearing
about how her work isreally at the intersection
of quantum science andartificial intelligence,
but also at the intersectionof academic research
and industry applications.
- And her personal storyis pretty amazing too.
(01:06):
You know, she's a scientistwho's now working in a startup.
She's trying to learn the business world.
Estelle just has a fascinatingpersonal story as well.
She's originally from Cameroon,
and she originally wanted to do something
completely different than physics.
We won't give any spoilers,
but her journey into physicswas really fascinating,
especially because she facedquite a lot of obstacles
(01:28):
in her native Africa tobecoming a physicist.
And we learned that she'sactually gone back to Africa
to try to help inspire otherwomen scientists there.
- We're excited for youto hear the conversation.
Let's step inside thePerimeter with Estelle Inack.
(upbeat music)
Okay. Hi, Estelle.
Thanks so much for sittingdown with us today.
(01:49):
- Thanks for the invitation.
- It's great to have you here.
- It's my pleasure.
- So you work in a really exciting field
that's also pretty new orat least rapidly growing,
that is often called quantum intelligence.
Can you tell us a little bit
about what draws you to thisfield and why it's so exciting?
- So it's basically a very fancy name
(02:10):
that means a lot of different things,
depending on how you takedifferent combinations
of artificial intelligenceand quantum computing.
So for example, for some people
it might mean using quantum computers
to perform artificial intelligence tasks,
in the field that they callquantum machine learning.
For other people, it couldmean using quantum computers
(02:32):
with artificial intelligencefor quantum control,
for example,for quantum state preparation.
For other people, it can mean
using machine learning techniques
that you borrow from AI research
to basically probe the behaviorof quantum many-body system.
And this is more of thefield where I am now,
borrowing machine learning techniques
to probe the behavior ofquantum many-body systems.
(02:54):
- You mentioned a few terms there
that I'm hoping youcan elaborate on a bit.
A lot of people have heard theterm artificial intelligence.
It's very much in the news.
I think a lot of people
have heard the term quantum computing,
maybe a little bit less soin the public consciousness.
Can you tell us what those are
and how you're sort ofbridging the two fields?
- Yes, so there are so manydifferent ways, as I mentioned,
(03:14):
and different ways ofbridging the two phase.
So quantum computers, for example,
is just a different way ofcomputing, a different paradigm.
It's using some of theproperties of quantum physics
to hopefully speed up some calculations
that are currently intractable
on the current class ofcomputers that we have.
Some people used to call that
(03:36):
like the second quantum wave revolution,
because already with thecurrent computers that we have,
we already use quantummechanics, transistors.
But now we want to use, to leverage
other properties of quantum system,
either entanglement,superposition, quantum tunneling,
to yeah, have some speedup on some algorithm
like Shor's algorithm, for example.
So it's a totallydifferent kind of paradigm.
(03:58):
Now a artificial intelligenceis in general, basically
thinking about having anintelligence that is not human
that is able to perform human-like tasks.
Right, and under it, you canactually write some algorithms
that we do that.
You have machine learning
and you can have neural networks,
that kind of generally people think of it
(04:20):
like representation of the brain,
even though sometimes it's not like that.
Even though it's remarkable to see
that some of the intuition behind things
like conversational knowledge works is
basically how we see, tobasically come with the design
of that kind of deepneural networks, basically,
to be able to do imagerecognition, for example.
So that's basically twodifferent communities
(04:42):
and a lot of sub-fieldswithin those communities.
And now within the sub-fields,
yeah, you can find some correlations.
I will tell you, for example,one of the correlation
that I'm mostly familiarwith in simulating
quantum many-body systems
on what we call classicalarchitectures like your laptop
or whatever cluster we areusing here at Perimeter, right?
(05:02):
So for us to be able to simulatequantum many-body systems
are different methods.
One of the popular methods isa quantum Monte Carlo method
called variational Monte Carlo.
To use that, you need to be able to have
what is called an ansatz,which is just a good guess
of what the ground state wave function
of your quantum many-body system is.
(05:24):
But to have this good guess,you need to understand
the Hamiltonian or the physicsof the problem at hand.
Is it fermions?
Is it bosons, right?
What are the interaction strength?
What is the Hilbert space?
Is it a Fock space, right?
And based on that, on thesymmetries of the system,
you come up with a good ansatz.
(05:44):
Now, not everybody can do that, right?
We really need very specialized knowledge.
And the moment youperturb the Hamiltonian,
that you go to another Hamiltonian,
maybe it's totally out of your field.
If you leave fermions and go to bosons,
you don't get the intuition anymore.
So the idea of neural networks,
that is borrowing like someknowledge from neural networks.
Since there are universal approximators,
and hopefully they shouldbe able to represent
(06:07):
any kind of function,
then why not representing then
the ground state wave functionof a many-body system.
That was the original idea of borrowing
this kind of neural network,
basically perform quantummany-body simulations.
And even though nowadayswe see that we still need
a little bit of quantum intuitionto make it work perfectly,
(06:27):
like you need knowledge ofsymmetries, for example,
we encode it in a neural network
to make it represent yoursystem in a much better way.
But yeah, so the story is
that, yeah, we saw how itwas working amazingly well
in machine learning.
And it is also startingto work quite well.
- You and I, Estelle, we actually work
in similar research areas.
(06:49):
You kind of said already, we're both part
of this Perimeter InstituteQuantum Intelligence Lab.
We have our matching hoodies today.
- Green hoodies.
That's because the acronymfor that institute is
- Is PIQuILs.- PIQuILs.
- They have to be, everything's green
at the PIQuIL.- PIQuIL.
- Yes.(all laughing)
- And I think somethingthat's pretty unique
about this group, at least compared
(07:09):
to maybe other researchgroups at Perimeter is
that there tends to bea lot of opportunities
for collaborations with industry.
So can you talk a little bit about that?
And what maybe could be unique
or what's important about these academic
and industry collaboration?
- Definitively. What is uniquefirst of all is the field.
The field, as I mentioned,
(07:30):
we are using a lot of state ofmachine learning techniques,
which we know industry use a lot.
Facebook, Google, theyhave huge research groups
that publish a lot of papers.
So already in that sense, we,just by using those tools,
we are already somehow inbetween industry research
and academia research.
(07:51):
- Are those classicalmachine learning techniques?
- Those are classical machine learnings.
Even though now a lotof those big companies
are having quantum groups as well,
and they are developing
quantum machine learningtechniques as well,
and a lot of startups as well.
So the field of quantum computingis being pushed forward,
both by academia and industry.
(08:15):
And the PIQuIL is trying tobridge, I mean, those two worlds
and to provide a platform
where academia can talk toindustry and vice versa,
and together working on the projects
we can speed with which we advance things.
- I often think of it like anarea with many bridges, right?
Because you're trying tobridge academia and industry,
(08:36):
but also quantum with machine learning.
- Exactly.
- Lots of different bridgesyou have to go over.
- Exactly, exactly. Andone interesting thing
that has come up in the last few years
is physicists are thinking
of actually importing some of the methods
that we have been using to quantum matter
(08:57):
to the machine learning community.
I think of tensor networkss, for example.
They're like, oh, we havea very good understanding
on these tensor networks.
We can interpret them
instead of using your black boxes.
So maybe you could use that
for, I don't know, image recognition.
And people have been doingthat and it's working.
So it is also a way forthe physics community
(09:17):
to somehow give back to the AI community.
- You mentioned that whatbrought you to Perimeter
in the first place waslooking at Roger Melko's work.
And now Roger is, he's the head of PIQuIL,
the quantum intelligence lab.
Can you just give us a senseof what it's like at PIQuIL?
What is a day like at the PIQuIL?
(09:38):
What are the sort ofquestions and problems
that are being tackled there?
- PIQuIL is really like astartup like kind of environment.
Even though there'sindustry and academia there,
there's a lot of free discussions.
We have journal clubs.
It was virtual during COVID.
Now we are starting to come back person.
A lot of discussions aboutSlack, "Oh, this is a new paper.
(10:00):
What do you think of?
Oh, I have a problem in my research.
Do you have a solution forthat" and things like that.
So really a lot of interaction.
- And so you first came here to Perimeter
maybe to pursue more theacademic side of things,
but as time has gone on,
you've become more and moreinvolved with industry.
And now you're actually the Co-Founder
and the Chief Technology Officer
(10:20):
of a company called yiyaniQ.
Can you tell us a littlebit about your company
and what it's trying to do?
- Definitely, maybe I will takea step back by a little bit.
I was doing my PhD and thenI was doing my post doc.
So I was mostly focused on academic work.
But even though I was focused on that,
my specialty is developingalgorithms to solve optimization
(10:44):
beside probing the behaviorof quantum many-body system,
but optimization points thatare like real-world problems.
But typically the way we solve it is okay.
Like physicists, we like tohave like a very easy model
that we can benchmarkand things like that.
That is not really reality.
It is not gonna affectthe life of somebody.
And so I always had behind mymind, in the back of my mind
(11:06):
that these algorithms, wecould actually try to use them
to solve real-worldproblems, not just write it
at the end of theconclusions of our papers.
And, oh, you can use itto solve a real-world.
So I had that in back of my mind.
And yeah, so last, Ithink one year and a half,
we had these very nice resultsof an algorithm we designed.
(11:28):
And we decided to basicallyfile a patent away.
And that was the moment I was like, okay,
now we need to try to commercialize it
and see whether we canhave real-world impact.
And we created yiyaniQ.
So the company right nowis focusing on designing
what we call quantum intelligent algorithm
to basically speed up derivative pricing,
(11:49):
which is a specific problemin quantitative finance,
in the sales side of financial market.
In the beginning, Iwas very much confused.
I had a hammer, I didn'tknow where I'd find the nail.
So there are so many differentoptimization point out there.
Some are very interesting.
Some are very challenging.
Others are boring.
I really needed to find onethat was challenging enough,
(12:11):
but I found that very fastthat, yeah, you need somebody
who has expertise tobe able to design that.
And I met him in an incubator
called Creative DisruptionLab, Behnam Javanparast.
And he has a PhD in theoretical physics,
in condensed matter as well.
So we could talk to each other,
but he also worked in a bankfor more than seven years.
So it was quite very easy for us
(12:32):
to kind bring ourperspectives to found yiyaniQ.
- I'm hoping you can tell us a bit more
about optimization problems generally.
Could you tell what the term means
and how you apply your techniques to it?
- Usually for us physicists,
it is useful for us tokind of map a problem
(12:54):
into a configurationthat we understand best.
And one sweet thing is that we can view
optimization problems as a search problem
in a very complex landscape,
where in an optimization problem,
typically you have a functionyou want to minimize.
Everybody more or lessunderstands functions,
but for a physicist,
(13:15):
I can see that function as an Hamiltonian.
Directly when you tell meHamiltonian, I was like, yay.
I have a lot of tools in my toolbox
to be able to deal with that.
And I can view theHamiltonian as a landscape.
You could imagine, forexample, in the Himalayas,
you have a lot of hills and valleys,
can be kind of very crazy landscape.
(13:35):
And solving the optimization problem means
from a physics standpoint is
finding the groundstate of the Hamiltonian
that represents that optimization problem.
But from a graphical point of view,
it means finding the deepestvalley in that mountain.
And for you to find the deepest valley,
you need to search, go up and down.
(13:56):
And depending on how you search,
you can be more efficientin finding the landscape.
But if your landscape, forexample, has a lot of valleys,
a lot of saddle points,
it has tall hills, right,
and maybe very wide hills,
it might be difficult for you
to be able to find the deepest valleys.
(14:17):
This is hardest search problem
where solving on optimizationproblem would be seen.
- Would it be similarto if you wanted to find
the deepest valley in the Himalayas,
you could walk up anddown all of these things,
but optimization is a way,
is an attempt to not put inthat sort of brute force work,
but find the simplest route to the answer.
(14:38):
- Exactly, it's finding thesimplest route to the answer,
which definitely what you justdescribed going up and down
could be mimicked with algorithms.
And it has been mimicked with algorithm.
The most notable oneis simulated annealing,
where going up and down ishaving some thermal energy
to basically overcome barrier
till hopefully, basicallyyou find the deepest minimum.
(15:00):
But imagine that you're goingup and down with your car.
Some moment, I mean, fuel is gone.
What do you do?
So in the simulation is
when you are ramping down the temperature,
and then yeah, there'sno temperature, no fuel,
which means no fuel, no kinetic energy.
And then you get stuck ina local minimum, right?
But you could think ofa different paradigm
which people have thoughtof using quantum computers
(15:21):
or using one property of quantum system
that is called quantum tunneling. Right?
Then instead of goingup and down the valley,
you basically tunnel through the hills
in the search of the deepest minimum.
And then that hopefullywill be a faster way
for you to find the deepest minimum.
This is not a crazy intuition,because when you think
about the way we build tunnels nowadays,
(15:44):
if you're like a buildingcompany and they say, okay,
you need to build like either rail tracks
or you need to build aroad through the mountain,
if you see that the mountainis for example, very tall,
but then the width is not that long,
you're not gonna build thesetracks on top of the mountain.
That doesn't make sense.
You build a tunnel, quantum tunneling.
(16:04):
So that's kind of the idea.
But at the same time, if your mountain,
the height is not that high
but it has a very like long width,
doesn't make sense to build a tunnel.
You just go over it.
So classically it's better.
So that's the reason why most of the time,
people do not carewhether quantum tunneling
or quantum annealing orclassical learning is better.
(16:26):
It totally depends on theshape of the landscape,
and the shape of the landscape depends
on the hardness of the problem.
- You told us that your company, yiyaniQ,
its main focus is using these techniques
on the problem of pricing derivatives.
And that's a financial markets term
that I barely understand.
I believe derivatives are contracts
(16:47):
between financial institutions
that are based on assetswithin these contracts.
That's about all I know,
but it's a difficult problem.
Pricing derivatives, I know,is a very difficult thing.
I'm hoping you can tellus why it's difficult,
how it's currently done
and how you hope to do itbetter and more efficiently.
- Yeah, that's a very, very good question.
Indeed, like we arefocusing on what is called
(17:09):
over-the-counter derivatives
that are mainly traded by verybig financial institutions.
And some of them, they'recalled like structured products,
they are quite complicated to price.
So the way it's currently being done
is using Markov chain Monte Carlo.
And for you to be able to price them,
(17:30):
you need to come up with a large number
of possible financial scenarios
that obeys the law of large numbers.
So the variance of yourestimator, of your price,
goes down with one over the square root
of the number of scenariosthat you can generate.
So basically you need togenerate a lot of scenarios
(17:50):
to come up with an accuracy
that satisfies a trader, for example.
That takes a lot of time.
So we talked to sometraders working at banks.
They told us that some of the books
that have a lot of underlyingproducts in one contract
can take from 60 to 90minutes time to price,
and they need to price it a lot of times
(18:11):
during the day, every day.
So not only it takes a lotof time, since they have,
like they cannot go beyonda certain amount of time,
which means they cannot pricea certain number of scenarios,
they have to reduce thenumber of scenarios of price.
It means they cannot havethe margin that they expect.
So they told us that sometimes
they could be mishedge of $10 million.
(18:33):
That's the error bar
of price.- $10 million error bar.
- Exactly.
- I wish I had that error bar.
- That's very huge.- Well, it depends
which direction it's in.
(all laughing)
- That's very huge.
So idea is basically becausewe know there are some methods
that are more efficient thanMarkov chain Monte Carlo,
be able to price it faster
(18:55):
and also more accurate.
This is what we are hoping to achieve.
So typically you want tofind the deepest valley,
but sometimes it's very hard.
So if you want, you find a valley
that is not so far from thedeepest valley, you're fine.
That's like they call itnear optimal solutions.
That would be fine as well.
Say for example, you're solving
(19:16):
the traveling salesmanpremise, as you mentioned,
if you don't find optimal path, okay,
the salesman will not be angry
if you find a near optimal path.
That saves him time and money.
- Right.- Right?
- He probably won't know
that it's not the actual optimal path.
- (laughs) He probably won't know.
- Yeah, that problem essentially is
how does a traveling salesperson
hit a certain number of cities
(19:37):
in the most efficient way possible.
And it's just a very difficultmathematical problem, right,
an optimization problem.
- Yeah, definitely,definitely, definitely.
So if it's not exactly solved,but approximately solved.
So for the financial case,what we are trying to do,
so the crucial part of our approach is
(19:58):
that we need to be able tomodel the financial problem
of derivative pricing as an optimization.
And then we can use quantum annealers.
We can use all kind of flavorof simulated annealing,
parallel tempering, whatever it is.
We can use variational annealing.
We can use mem, justvariational optimization
(20:21):
with neural network.
So that's where you reallyneed the financial expertise
to be able to cast it asan optimization problem.
That's our approach, which is different
from the approach peoplehave been having before
because we know for example,
there are algorithmson the quantum computer
to solve the price derivative,
(20:43):
like quantum amplitude estimation
on measurement-based quantum computers.
But we could use measurement-basedquantum computer as well,
because we know that the techniques
like QA, quantum approximateoptimization algorithm
can be used to solve an optimization
from a CP-based ormeasurement-based quantum computer.
(21:05):
But by looking at the currentstate of quantum device
with the qubit bonds,with the noise level,
we feel like for relevantreal-world problems,
we are not there.
So our approach was mainly focused
on an annealing-based approach,
plus physics inspired, plusmachine learning techniques.
- And the name of yourcompany is really interesting.
(21:26):
And I'm wondering if you'llshare with us the story
of what the name means andhow you came up with it.
- Definitely, so I like the PIQuIL so much
and the fact that our kind of motto
is kind of quantum intelligence.
So I wanted to have something similar,
but in my local language, to be innovative
and to differentiateself from everybody else.
(21:48):
But I don't speak my locallanguage very well (laughs).
So I kind of, I asked my whole family,
my mom, my dad, my brothersand my uncle and aunts
to come up with a name thatmeans quantum intelligence
in my local tongue called Basaa.
First they told me that quantum,they don't know what it is,
(Lauren laughing)even in English (laughs).
So we kind of put it out of the picture.
I told them, okay, somethinglike shell intelligence,
(22:12):
intelligence of the future,
something like that.
They came up with differentnames and my mom won.
She came with yiyani.
Yi, that means intelligence,and yani tomorrow,
which means the intelligenceof the future basically,
and the Q at the end.
- So intelligence, future, quantum,
it seems like a pretty greatname for what you're doing.
- Yeah, yeah, definitely.- Yeah.
(22:33):
- Do you remember any of thenames that didn't make the cut?
- No, my God, so many.
(all laughing)
- And so I know your companyhas grown a lot, as you alluded
to through this CreativeDestruction Lab program.
Could you tell us a littlebit more about this program?
- Yes, so basically itis like an incubator
(22:55):
for quantum companies.
In fact, they had a number
that about 25% of thequantum computing companies
passed through their program,
can you imagine, in the whole world.
So it's really like oneof the main incubators
of quantum computing companies.
I knew about it before, becauseRoger is very much involved.
I think he's the academic director of CDL.
(23:17):
So I already knew about that.
And when I decided to create a company,
I applied for the boot camp.
So they have a boot campusually during the summer
for about a month half-ish.
And so I went there.
There are a lot of course fundamentals
of quantum computing, quantum physics.
What are the current statesof quantum architectures?
(23:37):
There are so many differentway of building a qubit.
What are the current business cases?
What are the potentialadvantages and things like that.
And then you have a worldcore of quantum enthusiasts.
You could start a company
or you could, becausesome of them are startup,
you could join a company.
I got a lot like offers forexample, during the bootcamp.
(24:00):
But then yeah, so the idea of that is
basically helping people who have ideas
on using quantum computing technology
to solve real-world problems,
to basically groom them, helpthem navigating the landscape.
- And I know you have a lot of experience
working in the academic side.
(24:20):
But probably working in industry,
I guess there's a whole new skill set
that comes with working in this new field.
Were there any lessons
that were particularlyuseful from this camp,
as you tried to build this bridge
between academia and industry?
- Definitively, I stillwant to do research.
For me, the most shocking truth
(24:41):
is that businesses don'tthink like researchers.
I learned that they don'tcare whether you're using
state of art technology or new technology.
They just want you to solve a problem.
And so for me, when I thinkabout, oh, if I, for example,
improve an algorithm of an order
or two order of magnitudes, I'm excited.
(25:02):
If it does not translateinto them earning more money,
they don't care aboutthat, (chuckles) right?
So it makes me have a different approach
on doing research for business.
I have to do research, yes.
I need to think about usingthe best possible tools, yes.
But at the same time,
(25:22):
I need to think aboutpotential business advantage,
which we don't think about.
Of course, we don't think about that.
We are most interested insolving exciting problems.
- It's like optimizinga different function.
- Exactly.
(Estelle and Lauren laughing)
- Was the term boot camp applicable?
Was it pretty intense?
- Oh yeah, it was like, it was crazy.
(25:44):
And in fact, the craziesttime of the boot camp
was it had a two day hackathon.
I think I probably slept like three hours
during those two days.
You had to come up with an idea
to solve a relevant businessproblem using a quantum.
- In two days.- In two days.
- And any problem, or theytold you a certain problem?
- Any problem of your choice.
(26:05):
So they had some problems,that maybe some hints,
but any problem usingsome of the architectures
that were made available to us.
And yeah, program it andcome up with results.
So there was only, notonly the scientific value.
You need to come up with a business pitch,
like do some quick market research,
show that, come up with the numbers
(26:25):
that this is a relevant problem
and have a short videoof making your pitch.
- Hang on, you've got two days
to develop quantum algorithms
and a business pitch and a video.
- Yes.- Okay.
So when did you get thosethree hours of sleep?
(all laughing)
- I was working withBehnam until midnight,
I think.(Colin and Estelle laughing)
- Did you just crash at the end?
- It's when we stopped talking
(26:46):
around maybe midnight or 1:00,
and then I kept working till probably 3:00
and got up at 6:00, andstarted working again.
- And what did youactually end up developing?
- Oh, we basically wrote acode on the D-Wave machine
to solve a portfolio optimization.
And we had to push it on GitHub.
So it's available on CDL GitHub.
(27:07):
- So hold on, not only did you have
to come up with an algorithmand a business plan,
but then you had to push this out
and make it available to other people.
- Yeah, publicly available, yeah.
- And you mentioned D-Wave.
Can you explain a littlebit about what that is?
- Oh yes, so D-Wave is aquantum computing company.
It was the first one toactually commercialize
the quantum computer.
And so they are mostly focused
(27:29):
on annealing-based approach
as is solving optimization.
Even though recently, theyannounced that they are starting
to build also CP-based quantum computers.
So one of the cool thing that they did,
and a lot of quantum computingcompanies are doing now
is if I want to run simulationson a quantum computer,
I don't need to go and buy 10million, whatever the cost is,
(27:51):
- Thank goodness.- And come
and install it at Perimeter.
You can have access to it through cloud.
And so you have an API code
and you just, yeah, pass in parameters
and it spits you backbasically the results.
And you can even see which qubit
you have been using the quantum processor
to basically solve your problem.
- So you can implementyour algorithms on D-Wave,
(28:13):
but in the cloud youcan do it from anywhere?
- Definitely, yeah.
- It's amazing.- Oh, not anywhere.
It depends on where theyhave the clouds deployed.
I think now you can do itin North America and Europe.
South America, I'm not so sure.
Africa, I'm not so sure.
Probably in Japan as well.
So as they are expanding, theyprovide that cloud service.
(28:34):
- And as you've said, Estelle,
it seems like there's justso many different priorities
that you have to balancewhen you're doing this work
at kind of the intersectionof academia and industry.
And we had a grad studentfrom here in Waterloo
send in a question about that.
- This is Matthew Duschenes,
a student at IQC and Perimeter.
I'm wondering, how do you balance
coming up with novel research ideas
(28:55):
versus staying focused on yourspecific startup objectives?
- Nice question, very,very important question.
I ask myself thatquestion every single day.
(Estelle and Lauren laughing)
- Are you able to balance these things
or is it always a juggling act?
- In the beginning, it was so hard.
It was really, really, really very hard.
Now I'm kind of equilibrating roles,
(29:17):
dividing my time half, half,
not every week, but yeah,that's what I'm trying to do.
Because for the company,definitively I'm doing
like an application of my techniques,
but we are in a very fast-paced milieu,
whereby you need to be awareof whatever is state of art.
(29:39):
So you need to be on top of your game
as far as research is concerned.
So I need to keep an open eye
on the research world as well. That's why.
- Must be changing every day.
- Exactly. So it's not as before
that I could read archivepaper every morning.
I cannot do that anymore.
(Estelle and Colin laughing).
I can attend journalclubs, attend conferences,
(29:59):
and I talk to collaborators
to keep in touch with what is happening
as far as research is concerned.
I was groomed as a PhD student
that a problem isinteresting when it's hard.
I mean, if it is not hard,what's the point (laughs)?
So I really like takingon very hard problems
(30:21):
and if they're relevantto an everyday person.
- When you get stuck ona really hard problem,
what do you do to push through it,
to get past that obstacle?
- I do it very badly, usually(laughs), almost depressed.
Anyhow, but yeah, typicallyjust take a step back
(30:41):
and try to do something else.
I mean, go boxing.- Boxing?
- Go swimming or running,something different.
Sometimes involvestalking to collaborators
to get some of the ideas that they have
and coming back to it with fresh eyes.
(31:02):
- So you've been tellingus a lot of stories
of things that have happenedin the last few years.
And I'm wondering if we can maybe go
back a little bit further.
Could you tell us the story
of how you first gotinto being a scientist
or how you first decided topursue that type of path?
- I have a very non-typical path
to becoming a scientist.
(31:23):
Yeah, so right away Ishould make a disclaimer.
I didn't plan to be a physicist (laughs).
- This wasn't the lifelong dream.
- Nope, it wasn't a lifelong one.
Yeah. Well, I wanted todo naval architecture.
So I was advised during my high school
that for me to do naval architecture,
I needed to have a bachelor in physics.
(31:45):
So I got.- Sorry, naval architecture
is designing ships?- Ships.
- Okay.- Yeah.
It's very, very different.
- But maybe you're gonna use your methods
for naval architecture next?
I guess we'll see.
- Yeah, why not?
- Maybe there's an optimization problem
in naval architecture.
- Oh, naval architecture, I don't know,
but definitely in the maritimeindustry on ship route,
(32:06):
there is an opportunity for that.
I even thought about that,
either ship route or ship loading.
For example, imagine I have a big cargo.
It has to load on thousandsof different containers
on the cargo.
What is the best way for you to do that
to optimize the space?
I actually wrote analgorithm that, VNA, yeah.
(32:27):
- Shipbuilding and ship architecture,
where did this come from?
- Since my mom worked inthe maritime industry,
I was very much influenced by her.
So I wanted to do a job
that was related to sea, ocean, right?
But I wanted to do a technical job,
something that I coulduse some of the things
I was interested in, mathematics, physics.
(32:48):
So I found that navalarchitecture was the best,
but I was not well-advised.
I found out that you cannotdo naval architecture
with a bachelor in physics.
Then I wanted to do computer science
after when I found out Icouldn't be a naval architect.
But unfortunately thatyear in my homeschool,
they didn't open up amaster in computer science.
(33:10):
The only available master was in physics.
So I was like, okay, Ineed to go to school.
Let me just really startfor the master in physics.
And I like it.
It was very easy for me to do,
and I think I got first class
and then I got a scholarshipto go to Italy to do it.
- It sounds like you're still interested
in shipping and ships.
Is that an ongoing fascination for you,
(33:31):
the maritime industry?
- No, I think it after I wasso disappointed, I should say.
I was really, really, really disappointed
when I found out I was just missing that,
so that kind of died out.
But for computer science, yeah,
I'm mostly programming now.
Almost all of my day I'm writing code.
(33:53):
I kind of brought together my interest
in computer science andprogramming in my physics job.
- Well, we actually got a question
about how to combine programmingwith research in physics.
So could we play the next question?
- Hi, so I'm Hassan Conser from India,
and my question is alittle more career-related.
(34:15):
How do the fields ofprogramming and physics
mix like simulation machine learning,
and is it necessary to learn programming
when going into field of physics?
- From the first part of your question,
if you think about machinelearning for physics,
you definitely needprogramming for that, right?
But if you have to thinkabout physics in general,
(34:38):
it really depends onwhich field of physics.
There are some fields of physics
where not a heavy amountof programming is needed,
some even none, just need to do
some kind of analytical work.
But when you think aboutthe field of physics,
generally as a rule,
my feeling is that little bit skill
(34:59):
on just knowing how to plotfunctions is important.
Just knowing Python, whichis very easy to learn,
should be sufficient to get by.
But if you want in field ofcomputational physics, yes,
you need to know how toprogram a little bit more.
And nowadays it's really easy.
For example, for machine learning,
there are a lot oflibraries you can just use,
(35:21):
I think about 10 or so,
to write prototype of your model
and to test it very quickly.
You have things like Google Colab.
You can use GPUs tosimulate very fast things
and even get some results.
So I feel like it shouldn't be seen
as a very huge barrier.
Programming is actually very fun.
But my advice is that youshouldn't lose sight of the fact
(35:44):
that at the end of theday you're a physicist.
So you need to sharpenyour physical intuition.
I give you the advice
one of my lecturer gave mewhen I was doing my PhDs.
You first of all need totake your pen and paper
and figure out the physicsbehind the problem.
And once you do that, thenyeah, you can take your computer
(36:05):
and write some code.
- You've used the term physicalintuition a couple of times.
I'm hoping you can explainwhat you mean by that.
- Physical intuitionis based, I would say,
on the understanding on how nature works
and the understanding ofsome physical principle.
(36:25):
Take like the Heisenbergprinciple for quantum mechanics.
If you know exactly theposition of a particle,
you cannot know exactlymomentum of that particle.
So when you think about a problem,
you need to have these kind of things
on the back of your mind,
and that will help you notonly interpret results.
It will help you design models
(36:46):
to maybe benchmark somethingspecific about the model.
It's very, very importantto do top class research.
That's my impression.
- You've attended workshops in Africa
about promoting women in science
and just promotingscience in Africa overall.
Can you tell us why youwanted to attend those
(37:07):
and what you hope peoplegot out of your presentation
or your attendance?
- I always find myselfliving in a superposition
of two almost orthogonal worlds.
Unfortunately we knowthat science in Africa
is a little bit laggingcompared to the West,
(37:28):
but for women it's even worse.
It's like this really was
because a lot of cultural apprehension.
It's changing, it's reallychanging, but still sending women
to do what is called hard skills,
typically people think
that, okay, math, physics,it's just for men,
(37:49):
even when they're tryingto be progressive.
Even here in the West for women,
we see that as you go up the ladder,
we see less and less women.
It's even stronger in Africa
because there's morecommitment that is demanded.
And the role of the woman in family,
(38:09):
it demands a lot of your time.
That makes it very hard foryou to do top class research.
Starting to have these conversations,
one of the feeling that I've been having,
that it has to start firstwith women scientists,
African women scientists, the mindset,
to kind of recalibrate themindset that it is possible
(38:30):
for me to do science.
I don't necessarily need to create.
It is possible, and from there,
like put together policies.
I feel like this is very important.
Also educate our male counterparts,
starting with our families toreally change that mindset.
(38:52):
But me in my career, Ihad a lot of instances
of people telling me that,why are you doing your PhD?
You should be married and having kids
and preparing for your husband, (laughs)
right, this kind of a thing.
But I was educated in thehouse when my mom told me
that as a female, you cando whatever a man can do.
I already had that in my mindset,
but other people, they don'thear that kind of thought.
(39:14):
It can really affect them.
But starting to have this conversation,
we hope to see change.
- Is that what you're tryingto do when you go there?
You're trying to help
with this recalibrationprocess for individuals.
- Even not necessarily when I go there.
Whenever I happen to interact
with female scientists from Africa,
(39:37):
which happened once in a while, yes,
trying to have those conversations,change of the mindset.
It doesn't have to be this way.
- I read that your father,
he'd wanted to get a PhD in physics,
but he didn't 'cause thereare more practical paths.
He chose engineering, I believe?
- Yes, actually it was nottoo much of his willing.
(40:01):
So my father was, he is still very smart.
He was very smart.
So he had a government grantafter his high school degree
to go to France, to basically to study.
He was studying physics.
And then he wanted to do a PhD in physics,
but the government was paying his stipend.
It's like, we don't need physicists.
We need engineers.
For him not to lose his scholarship,
(40:23):
he had to move to engineering.
But then he really encouragedme a lot to do physics.
- So what was his reaction
when you obtained your PhD in physics?
- He was very happy.
In fact, he told a storyduring my PhD party
of the fact that when I was doing
first year bachelor in physics back home.
(40:45):
So I did in high school, French education.
I studied in French.
And naturally I'm like French speaker,
my mother tongue, willing God.
But then I moved in English inthe Western part of Cameroon.
It was very, very hard.
I needed first to understand the English
before understanding the physics.
It was, I had a dictionary all the time
(41:06):
when I was going to the lecture.
So it was really, really bad.
One month after startingmy bachelor in physics,
I passed an engineering concours
in the French side of Cameroon
to become an engineer.
So I called my dad.
I was like "I'm stopping this thing.
It's not going; I need togo and do engineering."
My dad told me that "No,Francophones have been able
(41:28):
to go to that school and graduate.
You're gonna stay there."
I was so mad at my dad.
I was so angry.
But after a couple of months,I picked up the English
and I did very well.
And he told the story during my PhD party
that I hold strong and nowshe's a PhD; she's a doctor.
So that was sweet.
- That's beautiful.
- And Estelle, you've toldus so many nice pieces
(41:50):
of your story starting in childhood.
And I know you kind ofhave alluded to the fact
of how you made the decisionto come here to Perimeter
for a postdoc after your PhD.
But I know that you actuallyhad a lot of options
for what to do after you had a PhD.
And I always look back fondly
when you were making that decision
'cause you and I actuallytalked before you came here.
(42:10):
And so I always like to tell people
I was one of the first tomeet you here at Perimeter.
So could you tell us a little bit more
about how you made that decision?
- Yes, definitely.
So one thing I wanted to make sure is
that people I would be workingwith, especially Roger Melko,
I already knew he is a great scientist,
but I wanted to know that he'sa good person to work with.
(42:32):
So I wrote to you and sent you an email
and you were very nice to havea Skype discussion with me.
And I was just convinced.
There's also Giacomo who sent me an email,
who replied to my email.
He told me that it'samazing to work with Roger.
That convinced me thatPI is a great place,
but at the same time I hadopportunities, one in Alberta,
but they called, canceled it out.
(42:55):
There was one at Microsoft,
which was actually themost interesting one.
I had one also in California,which was kind of interesting
because we have collaborationwith people at NASA,
but then I'd be workingmostly on developing further
the algorithms that Ilearned during my PhD.
At Microsoft, I would'vebeen applying the algorithm
(43:15):
I developed during myPhD on WeWork programs.
That was extremely exciting for me,
but I wouldn't have learned
something really new,
would have been mostly application.
Whereas here at Perimeter,
I would have enlarged my research interest
to include machine learningand neural networks.
So that is basically the reasonwhy I chose to come to PI.
(43:38):
And I don't regret that at all.
- And Estelle, now at Perimeter,
your title is Research Scientist,
but when you first came here,
you had the title ofa post-doctoral fellow
under the name FrancisKofi Allotey Fellowship.
Can you tell us a littlebit about this fellowship
and how it was named?
(43:59):
- So Francis Kofi Allotey,
unfortunately he passedaway about five years ago.
He was really a monumentof an African scientist
who literally inspired andtrained generations of physicists
on the African continent.
So he actually did a graduate degree
(44:21):
at the Imperial College London
under the Nobel Prize Winner, Abdus Salam,
who later on created
this famous, the AbdusSalam International Center
for Theoretical Physics.
And then he did his PhD
at Princeton under Robert Oppenheimer.
- He did his PhD with Robert Oppenheimer.
- Yeah, so he was the first Ghanaian
(44:43):
to do almost everything,
the first Ghanaian to earn aPhD in mathematical physics,
the first full professor
in mathematical physics in Ghana.
And as far as research is concerned,
he is mostly known forthis Allotey formalism,
which is basically a wayto detect soft x-rays
in material like lithium orother alkaline materials.
(45:08):
And yeah, so he kind of, I mean, he has
a single authored paper onthat, which is pretty neat.
And he got, I think, a medal for that.
But beside his researchcontribution he had,
he was member of a lotof international bodies.
He created, was one of the founding member
(45:28):
of the African Physical Society.
He was a board member at ICTP
and a lot of other institutions.
And he did a lot of work as well in Ghana,
like creating institutes
and fostering likescience in the continent.
So it was quite an honor for me
to have a fellowship named after him,
(45:51):
almost more than myshoulders could bear, right?
But it was good also forpeople in the West to see that,
because typically you're not familiar
with that kind of a name.
You think more like Einstein,
Dirac, Schrodinger,
but it's good to see thatwe also have, if you want,
(46:12):
we can groom up top class scientists.
So to how did I come up with the name?
So when I was coming to PI,Neil Turok actually gave me
the choice, interestingly enough,
to choose which name I wanted.
And yeah, I chose him.
- Now, you've kind ofexpanded this set of tools
(46:32):
that you have through your postdoc.
And now you're getting to work
on maybe some of thesereal-world applications.
And I really liked thething you said earlier
about how often in papersacademics are claiming
that their methods could be applicable
to all of these potential,huge, real-world problems,
but maybe people don'talways really put the effort
into solving those.
And it seems like that's reallywhat you're trying to do now
(46:55):
in your work, which is really impressive.
And I'm just curiouswhat other real problems
you have in mind to look at next?
- Oh yeah, maybe not to look at next.
Kind of the biggest problemI have in mind to solve
is protein folding.
- Protein folding?- Yes.
- What is protein folding (chuckles)?
- So basically a proteinto be functioning,
(47:16):
it has to have a certain conformation.
It can take millionsdifferent conformation.
When you're doing protein design,
you need to find aconfiguration white box.
They call it like whenthe protein is native,
in its native state orin its folded state.
And usually it startsfrom an unfolded state.
(47:39):
And the path through the folded state
is like you going through the Himalayas,
is a very, very hard path.- Peaks and valleys?
- Exactly, exactly,
with a lot of like localminimal saddle points.
It's very hard, and notonly is it a hard problem,
it's very relevant, drug design
to help us like have better drugs
(48:00):
and help in the health sector.
So this is also, it'shaving a very strong impact,
but it's also very hard to solve.
So this is one of theproblems that I'm thinking.
I know that the approachwe are having now is
really to develop state ofoptimization algorithms,
that of course, in the company right now
(48:20):
we apply it in the financial domain,
but that we can easilyexport to other domains,
like in the domain of protein folding
where the only subroutine or function
we will have to changeis just the Hamiltonian.
But then we will need tohave domain knowledge.
And how do you write theprotein folding Hamiltonian?
And that actually is nota minimalistic model,
(48:44):
is not an easy model,
but it's really a modelof a real-world model.
I cannot; you reallyneed somebody who works
in either quantum chemistry
or biotechnologicalsector and things to be.
- Just to see if I'm understanding,
right now when you're working,
Behnam, your co-founder
is kind of bringing thisexpertise of the finance side.
(49:06):
So you would kind of need
someone analogous to Behnam for this
protein folding problem.- Exactly, definitely.
So we even talked about that with Behnam,
that once we will be able to create value
with those algorithms, we start exploring
what we call in businessjargon other verticals.
(49:26):
- Well, it seems like there'sa lot of potential options
for the future, and I'mreally excited to see
what you're gonna optimize next.
- Thanks.
- Thanks for chatting with us today.
It was really fun.
- My pleasure.
(upbeat music)
- Thanks so much for steppinginside the Perimeter.
Be sure to subscribe so youdon't miss a conversation.
(49:49):
We've interviewed a lot ofreally brilliant scientists
whose research spans fromthe quantum to the cosmos.
And we can't wait for you to hear more.
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please give us a rating or a review
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