- There was a point where I was like,
if I can do it,
(bell dings)
anyone can. (laughs)
You can do it.
Little Morty says you can.
Oh shame, he's sleeping.
(kisses) Oh my God, I feel so bad.
Okay. Sorry, go back to sleep.
(upbeat music)
(shutter clicking)
(upbeat music)
Hi, I'm Amira, and I'm a researcher
in quantum machine learning at IBM Research
and at the University of KwaZulu-Natal.
And previously I worked in finance,
and I studied a financial undergraduate degree.
And then I found the world of quantum computing,
and I thought, wow, this is just amazing.
And coupled with machine learning,
this was just the thing I knew I wanted to do.
But the question that I get asked
almost every day (bird cheeping)
by someone different is how did I make this transition?
How did I go from basically no math
to quantum machine learning?
And in this video, I wanna give you a few set of resources
to answer this question,
as well as a few tips I wish that someone had told me
a while ago, starting out in the field.
(electronic chiming) The first category
is shaping your mindset.
I think this is really, really important at the outset
to realize that it's not easy to get into the field
of quantum computing and quantum machine learning,
but you definitely can if you believe it, right?
So, and I'm living proof of this.
The math is not easy, but a year or two ago,
I didn't even really know what a matrix or a vector was.
And now these things are like a second language to me.
So it takes persistence,
and you need to be prepared for that,
and you need to be positive about that.
So that's the first step of advice that I would give.
Step two is the math. (electronic chiming)
So even though we're going from no math
to quantum machine learning,
unfortunately quantum machine learning
requires a lot of math.
But luckily, quantum computing and machine learning overlap
in the mathematics that you need.
And this relies or rests heavily
on something called linear algebra.
And linear algebra sounds super scary,
but thanks to the world that we live in today,
there are beautiful resources
that teach mathematics like art,
which is how it should be taught.
And so my recommendation
is to start with this beautiful YouTube series
by 3Blue1Brown called "The Essence of Linear Algebra".
Once you understand how to think
about objects in mathematical spaces
and your intuition is there,
this makes enrolling in any edX or Coursera course
on linear algebra so much more palatable.
So I suggest starting there.
Then the next step
is to get the basics (electronic chiming)
of machine learning.
So there is an overwhelming amount of literature out there.
And my favorite place to start
is with the "Deep Learning" book,
which is available for free online by Ian Goodfellow
and a few other authors.
This book covers the basics,
as well as a lot of advanced concepts in machine learning.
And the reason I would suggest
starting with machine learning first
before quantum computing is because machine learning
really shapes your mindset in a different way.
You think about problems in a machine learning setting,
and you understand data differently.
You understand concepts that come up in machine learning
that you will need later in quantum computing
and quantum machine learning.
So I would suggest starting with machine learning first
and covering this book.
Then we can move on
to quantum computing, finally. (electronic chiming)
And in quantum computing,
my favorite resource is the textbook
by Michael Nielson and Isaac Chuang
called "Quantum Computation and Quantum Information".
This book is amazing.
It is the Bible of quantum computing,
and I still refer back to it today.
The first two chapters are about the history of computation
and quantum computing.
Then it covers some linear algebra
which will be a nice refresher.
And then it goes into quantum algorithms
and quantum computing.
And at first, this might be a bit overwhelming,
but read it again, and again,
and I promise you eventually it will start to make sense.
There are also hands-on resources
in quantum computing like the "Qiskit Textbook",
which is accompanied by code,
and if you're a Python programmer,
this will be already nice way to go as well.
And there're also great edX courses
like "Quantum Information Science"
which is also by Isaac Chuang and Peter Shaw
who is pretty famous in quantum computing too.
Then lastly, we can finally get
(electronic chiming) into quantum machine learning.
And my favorite resource here is again a book
called "Supervised Learning with Quantum Computers".
This is by Maria Schuld and Francesco Petruccione.
This book covers almost everything in the field.
And even though quantum machine learning
is very fast paced, it does a very good job
of providing an overview of the latest research
as well as the basics of quantum machine learning.
And I think the second revision is coming out soon
that will be called
"Machine Learning with Quantum Computers",
so keep an eye out for that on the Springer website.
(driving music)
And lastly, I just want to leave you with a few tips.
Tip number one, don't start with research papers.
This was definitely the biggest mistake that I made.
I thought that it would be easy
to just Google some quantum machine learning research
and read through it.
But the reason this is not so efficient
or not a good way to approach the topic or the field
is because research papers are highly specialized.
They contain a lot of jargon, a lot of language
that you won't necessarily be familiar with
unless you go through things in a systematic manner.
So you get the terminology from the mathematics,
you get the terminology from machine learning,
and then quantum computing,
and this makes then digesting research papers a lot easier.
So I would suggest not to start with research papers.
I remember I tried this when I was just starting my master's
and I had to use Wikipedia for almost every second word
of the abstract of a paper.
So this is really an inefficient way to go.
The next tip is quite an important one.
I think that it's very crucial
to set your expectations correctly
about quantum machine learning.
The field is very new and there's a lot to discover,
which makes it very alluring, but there's also frustration
in that we can't always show immediate results
like we can in classical machine learning
when things are moving so rapidly,
and there's always something new that we can test,
and we have the computational power to understand it.
In quantum machine learning,
we might not even see the effects of our research
in our generation.
And so this is something that could be quite problematic
for you if you are a hands-on person
that wants to apply your work all the time,
this might not always be possible.
So set your expectations correctly,
and the list of resources that I just mentioned
in no way will make you an expert in the field.
If anything, it's just to get you started with the basics.
The last step that I wanted to leave you with
is the importance of mentorship.
And this is something that I never realized
until I undeservingly received the mentorship
of Maria Schuld.
I was very lucky to have her as a PhD supervisor.
And having someone in the field to guide you,
to just give you ideas, for you to probe their thoughts.
This is invaluable.
And whether it's a person, or a community,
or even a Slack channel that you can join.
So Qiskit has a wonderful community,
and they are four main quantum machine learning groups
all the time.
The QOSF mentorship program,
Quantum Open Source Foundation Program
is also a great opportunity
to find mentors in quantum computing.
So this is something that I highly recommend doing.
Otherwise, good luck
on your quantum machine learning journey,
and I hope to see you
at further quantum machine learning events.
Thanks for watching this video.
I hope you found it helpful,
and don't forget to subscribe to our Qiskit YouTube channel
for more quantum machine learning
and quantum computing content.
Till next time. (laughs)
(gentle guitar music)