Quantum computing is rapidly advancing, moving beyond theoretical concepts to practical applications and industrial relevance, with significant global investment and a growing demand for a skilled workforce to drive its development and adoption.
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Hello everyone, welcome to the fifth
rerun of our course introduction to
quantum computing quantum algorithms and
kisskit. I'm Dr. Anupamare. I'm a senior
research scientist at IBM research
India. I'm also the technical lead for
AI for quantum for uh IBM quantum.
Welcome you all to this course. And
today what we really want to discuss in
this video which is ungraded is what is
it that we are wanting to do in quantum
and why is this all about? Why are we
learning quantum today? What is the
potential impact? Potential impact is
quite near-term in terms of making
compute industrially relevant. So what
is it that we want to do in quantum
computing? We want to go towards quantum advantage.
advantage.
In the center of your screen here you
see this white circle. These are
classically easy problems. For example,
sending an email or doing a video
conferencing or many such problems which
are uh easily solved by classical
computing or by AI. But there exists a
wide number of problems in this gray
ellipse that cannot be solved by
classical or even AI supercomputing and
never will be. A part of those problems
are quantum easy and can be solved by
quantum. And what is most interesting to
me is this new section of problems which
we were earlier not able to touch or not
able to think. These are quantum easy
problems which we were not unable to do
using classical computing. So in all of
datadriven sciences there is a rich seam
of problems that we will able to try or
we will able to design or solve uh via
quantum computing that is intractable to
classical computing.
A lot of people ask when will this
happen? I would say it has actually
started. Organizations across industry
and fields are accelerating their
investment in quantum computing with an
unprecedented pace. If you look at this
graph, you'll see that the number of
active PC's by different industry and
the industry sectors are colorcoded
here. That has been increasing. In the
last 3 years, it has increased over 50%
in the enterprise use case activities in
quantum PC's.
Investment in quantum computing is
accelerating worldwide. There's a lot of
global investment. For example, in 2018,
the National Quantum Initiative Act
authorized 1.2 2 billion investment in
funding over 5 years in the US. In 2019,
France formalized the French quantum
strategy with a budget of€1.8 billion
over the course of 4 years. In 2023,
Germany released its quantum technology
plan, investing €3.3 billion towards the
development of a universal quantum
computer by 2026 in an effort to build a
quantum ecosystem and a quantum
industry. And the list goes on with
Australia and so many other countries.
Everyone creating Japan, everyone
creating their own quantum plan
and so does India. India announced its
national quantum mission in the finance
budget in 2020 for the first time. Then
there was a sanction of 60 billion which
is an investment over the course of 8
years from 2023 to 2031. And we have
multiple goals that we are trying to
achieve. India should definitely have
its own quantum computer. So the first
thing is building the quantum computer a
50 to 10,000 cubit machine build on any technology
technology
but that needs to be built. In quantum
communications we want to have an
intensity QKD over 2,000 km. In quantum
sensing we want to be able to develop
high sensitivity sensors and materials.
For this effort, we have been creating
these hubs at four different places in
India and working actively.
The next goal which is workforce
development and that is part of what we
are doing here.
India has to produce 100,000 people who
are trained quantum developers by 2030.
In our course itself in the last four
runs we have skilled 37,000 people. This
year we have more than 50,000 people who
have already registered for this course.
In terms of number of startups we
currently have 60 plus startups in India
already a large number of them here in
Bangalore and the states have been very
supportive. For example, Andhra Pradesh
government has already called out and
supported that they are going to support
100 plus startups and and so on in other
states and the target here is to have
200 startups by 2030.
There's a large number of research
papers that are using these type of
hardares and softwares produced by
different uh quantum vendors. Of course,
as a result, uh, research in quantum
computing is proliferating with IBM
leading in quantum hardware and
software. And since 2016, when we first
made our quantum computer accessible via
the cloud, the scientific community has
leveraged our quantum technology, making
groundbreaking advances in in their
fields, publishing several papers, and
we have over 5,000 papers that we have
been able to publish on on our hardware.
and uh of course using our software.
When we think about what constitutes a
useful quantum computing, there are
three key milestones to us. Quantum
utility and we were able to establish
quantum utility in 2023.
What do we mean by quantum utility? We
were able to run these quantum
experiments reliably multiple time
repetitively. we get the same results
across different hardware at different
noise levels which are better than
classical brute force mechanisms
in 2026. We are looking forward to be
able to demonstrate quantum advantage
along with our partners and in 2029 we
have already put that in our road map.
We'll be delivering a first large scale
quantum fault tolerant quantum computer.
But why do we say we shouldn't wait for
fault tolerance? Understanding noise is
critically important. It helps us build
better error mitigation algorithms and
better error correction algorithm. And
fault tolerance actually doesn't mean
that there would be noiseless quantum
computers. It still will have some
noise. It's just that there is some
fixed error budget. The noise thresholds
are uh low and real time we'll be able
to um correct those errors. Now in order
to build those correcting algorithms we
need to start now.
So if we start now then we'll be able to
expand our error handling capabilities
and create better fall tolerant quantum
algorithms once the fall tolerant
quantum computers are built.
I'll just show you two graphs on on the
usage of these devices.
Couple of years ago people on an average
were I mean I mean the best uh number of
cubits you would see is less than 100.
Now it has gone to over 155 or 150 plus
number of cubits people are actually
using. So people are starting to use
more number of C cubits starting to
build these utility scale experiments
with different algorithmic approaches of
course and this uh graph on the right
stands for the circuit sizes that is
what is the largest circuit size that
people are being able to run. So that
impacts uh the number of gates and the
circuit depth. And if you see there are
very very deep circuits more than 8,000
gates that are being I mean 8,0002 cubit
gates. So very deep circuits that are
being able to run reliably on these hardware.
hardware.
We are already seeing first glimpses of
quantum advantages as researchers and
developers run these circuits that test
the limit of classical computing. We see
these as hypothesis of quantum advantage
and this work is setting the stage you
know for the back and forth from which
true quantum advantages will emerge.
We see two parallel paths. One is an
empirical test of quantum usefulness
which is for example a top it's it's a
top- down approach uh where quantum
scientists and quantum developers are
applying huristic methods that work well
for real world problems and these
explorations will help us realize
quantum advantage for practical problems
sooner for example the HSBC work where
they are showing a 34% improvement in
prediction of uh closing of a of a bond
price uh there's a 34% improvement with
the quantum algorithm and this has been
rigorously tested over couple of months
across different hardware on the same
hardware at different times of the day
and different multiple days across a few
months and on different hardware and you
have consistently got an average of 34%
improvement over the state-of-the-art uh
classical algorithms.
So that proves that empirically or
experimentally it proves that okay this
particular advantage is definitely
has an advantage over the classical algorithms.
algorithms.
Similarly we have got very good results
from some of the work with Cleveland
Clinic with modern on the mRNA problem
with Vanguard and so on.
The second path that you see here is you
know while we are empirically coming up
with these advantages it is important to
build trust in the quantum computers and
the quantum algorithms that it is truly
doing something beyond classical. We
need to be able to identify the kind of
quantum circuits that we'll be building
uh that can offer a verifiable
advantage. So uh there is a lot of work
where people are trying to come up with
this rigorous proof of advantage and
there's a bunch of papers that I've
listed here but there's a lot more that
is coming and in order to track all this
we have you know uh created a communityr
run tool alongside flat iron blue cubit
and algorithmic Q for the moment but yes
we are we welcome more partners and we
are soon going to have a lot more
partners. We want to be able to have
this tool to track progress towards
quantum advantage. So people like users
we can systematically monitor and uh
evaluate verified demonstration of
quantum advantage. You can put up you
can submit your results here. Uh you can
put okay this is my circuit and this is
the best result that I got. This was the
classical resource. This is the quantum
res resource and let people see how you
know they can they can try to you know
get better at it. So this is a this this
particular tracker shows how these
candidates stack up against the leading
classical methods.
Now because people talk a lot about
fault tolerance and all this buzz, it is
hard to know what these words really
mean. So let's break it down. First word
people say what is is large scale. So
what is large scale? Large scale is
hundreds of cubits capable of running
hundreds of millions of gates. So that
is the scale which we call large scale
right that has to be the scale beyond
that is the scale where you know you
cannot do classical simulation and now
you want to be able to run circuits
thousand times deeper than what is
possible on the devices today.
The way we are going to get there is
quantum error detection. And in this
course we are going to delve in the
fourth week a bit into error correcting
code and error detection set of error uh
set of these are basically a set of
techniques using error correcting codes
to encode quantum information not into
one physical cubits but in a number of
physical cubits. And there are several
ways to do it. A lot of people are
simply uh taking two or three physical
cubits and doing a parity check between
them to detect errors. that won't get
you to hundreds of millions of gates. Uh
if you're only detecting errors, the
overhead of course is exponential. But
the same stands for error mitigation. If
you uh want to run that many gates, you
will still need ridiculously good
physical cubits and you need to run the
circuit billions of times to get the
correct answer. In fall tolerant
computation, you'll correct the errors
in real time as they occur. Okay, so
fall tolerance is all about this
computing capabilities. And when we talk
about computing capabilities, I really
want to bring this to your attention. If
you are running some quantum experiments
with 10 cubits,
uh you can run it on your classical
computer. So whatever you're doing a
quantum experiment which you're
simulating on your classical computer,
you can you can run it and you will not
get any potential advantage because for
10 cubits you only need um uh some small
amount of 16k RAM, right? For 30 cubits,
a modern laptop with 17 GB RAM will
work, will be enough. You can use your
entire RAM and you can still do
somewhere around 20 to 30 cubit kind of
experiments. But this is exponential and
so for 31 cubits you will start to need
two laptops and very thing very quickly
things will get crazy.
This is why you know using classical
computers to run quantum problems is
actually a dead end.
So IBM Summit is world's one of the most
powerful supercomputer and if you are
running something around 48 cubits or 49
cubits you'll need something like summit
you want to run something at a scale of
60 cubits what a what 60 cubits can do
if you want to run that on a classical
device you will want to have all
classical computers on the earth
connected so one big device all atoms
you want to exhaust what you get to do
is what you will be able to do via means
of 60 cubits and that is the example as
a standard example of caffeine that you
know a lot of people would give caffeine
needs around 64 cubits for that you need
all atoms in the world and so on and at
100 cubits you would need a supercomput
about 10 trillion times larger than the
frontier to represent its states
so so you this is a dead end that's the
reason why we say that okay you cannot
use classical computers to run quantum
problems or quantum algorithms or
quantum circuits.
Now this particular chart is is is very
interesting to me. So on your yaxis is
the circuit width or the number of
cubits and on your x-axis is the circuit
gate size or the number of gates and and
if you see starling is here. So it's the
fall tolerant quantum computer which is
coming here in 2029
and blue J of course is a very large one
that's in 2020 2033 [clears throat]
and blue J is the one which is actually
you know you you say that that's the
realm of say actual chemistry advantage
or for short algorithm and the best
optim quantum optimizations and so on.
Now this uh going by this assumption you
should be able to actually see that
advantage in 2033 and that has an
assumption that your algorithms are not
going to get anything better only your
hardware will get better and better and
then potentially you can be here but
that's not true. Algorithms are going to
get better right? So algorithm will
definitely get better and hardware will
also get better. So this will this
advantage point is not going to happen
in 2033 but it's going to happen way
earlier somewhere around 2026 is what we
had predicted that we will be able to
see these advantages but we are already
seeing a lot of these advantage use cases.
cases.
[sighs] So that means in 2029 we'll be
delivering the world's first large scale
fault tolerant quantum computer which we
call Starling.
So today if you see this is the IBM
quantum system 2. This is the one that
is coming to India in the next 6 months
and this is Starling. What you will
finally see is Blue J will look
something like this which will have say
2,000 logical cubits or 1 billion
quantum gates can be successfully run in
this system.
We have been very clear about our road
map and I'll just present very briefly
to all of you the road map that we have
articulated. We have a development road
map and an innovation road map. Um and
and this is our development road map. So
on the bottom layer you see the hardware
which is in black. This is the kind of
hardware that we have been putting out.
We had put our first quantum computer
available for anyone to access around
the world in 2016 and it was a 4 cubits
very small machine. From that we went on
to 20 cubits, 53 cubits. Uh we have a
bunch of devices and recently we have
released something which we call
Nighthawk. It's a 120 cubit square
latice. It's a beautiful device which is
being able to do things way better than
the 156 cubits can do. On top of that
layer is something called Kiskit runtime
which is how can I accurately and
efficiently run my quantum circuits on
this hardware. So it is very closely
tied to the hardware. On top of that you
have a set of tools which we call as
orchestration tools which helps us in
doing resource management for example
Kiskit serverless plugins for HPC so
that we can envision and work our uh
plans around quantum supercomputing
and then on top of that is the actual
algorithm discovery. We have a bunch of
algorithms that we build on top of it.
Finally that get gets into our algor
algorithmic researchers who do not have
to or our application people who do not
need to understand everything in the
stack below to be able to run these libraries.
libraries.
So this is Nighthawk. Nighthawk uh is a
square latis. Earlier we had heavy hex
lattice. This is a square lattice. This
supports more efficient circuits with
fewer gates. It has a way better uh you
know u information routing. And we are
planning to scale even Nighthawk uh
significantly. So it would be with with
the help of uh connecting these chips.
Each chip has 120 cubits but these chips
will be connected by means of classical
communication and quantum communication.
And then we can actually stack them to
1,000 cubits.
And of course then finally we'll debut
uh Starling which will be able to run
circuits with 100 million gates on 200
cubits and then Blue J which can run
2,000 cubits u very reliably.
Now this has been the next layers uh
where you see there is a lot of focus on
orchestration of workloads, quantum
centric supercomputing uh then you know
our resource uh management and of course
discovery and new algorithms which will
lead to quantum advantage and then
finally applying those algorithms for applications.
applications.
This is our innovation road map which
has all the details of what we have been
doing. And uh one point that I really
want to mention here is you will see a
lot of people asking what is the number
of cubits? What is the number of cubits?
Next time people ask you uh or think
okay a better quantum computer is which
has the higher number of cubits. Do keep
it in mind. Scale is important but
reliability is even more important. So
what we did as I said we had started
with four cubits in 2016 that we put out
for everyone and then we went out and we
kept discovering bigger and bigger
systems. Osprey in 2022 is a 433 cubit
system and then when we went to Condor
we had a 1124 cubit system. So we know
how to scale. We can pack in more and
more cubits in the same chip. You will
ask me why are you now going down to 120
cubits? uh much smaller than 1100. The
reason is 1100 cubits with more noise is
not as good as 120 cubits with more
connectivity and significantly lesser
noise. Then if I can have these each of
these chips with significantly lesser
noise and then connect them via means of
quantum connections to other chips and
increase my stack increase my number of
cubits and have a very reliable
execution of quantum circuits that
should be the way to go. So scale is
important but scale is not the only
parameter. You have to consider
performance which is speed of execution
and reliability which is how good your
execution is which is how low your error
is. So next time anyone tells you that
okay how many cubits and how many cubits
please ask them that okay how many
cubits and what is the error rate per
cubit what is the error rate of the of
each uh connection what is the
performance what is the speed so that is
very important when we look at hardware
innovation of course on top of that we
have been doing a number of things in
software innovation for example our
software toolkit KisKit has been
something which has constantly been
evolving in this course we'll give a
sneak peek to kiskit
We have been building a lot of
application modules. Now in order to
make Kiskit leaner, we have put these
application modules separately out of
Kiskit and they are in Kiskit ecosystem.
We of course have something which we
call KisKit runtime. This is a
performance abstraction uh which
directly works with the hardware and how
can we run these circuits uh better on
the hardware. Then we have uh something
which we call quantum serverless which
is how can I manage quantum and uh you
know classical computes on different uh
computing and have this workflow orchestration.
orchestration.
We have a lot of work which uh we do
under the pillar we call AI enhanced
quantum wherein uh things like can I
build better uh transpilation strategies
for my quantum uh circuits using some AI
algorithm and we have been using
classical reinforcement learning to come
up with better circuit transpilation
algorithm or can I actually have
something like kiskuit code generation
via means of using AI there's a lot of
work in terms of HPC quantum integration
and one major thing is uh we now have
KisKit uh C API which can help you run
things on C++ and C which is something
critical for the HPC community. We also
have a lot of work in the advantages or
finding the potential circuits that uh
will have quantum advantage and of
course we demonstrated a realtime error
correction decoder uh which is ahead of
time and I'm very proud of the team that
they actually uh achieved it which is
something that we had targeted by end of
2026 but we have already achieved it.
So we have what the whatever the road
map you see is something that we have
already achieved
and with that I'll I'll take a break and
jump into uh the next topic which is
something very critically important for
all students and students keep asking us
at every place that we go that we study
these courses or you tell us to study a
set of other courses after this
introductory course but what do we study
this for? What are the careers there?
And in one of my keynotes that I was
doing, I had to prepare uh what are the
careers in quantum computing. And I
thought I'll use this to give you guys
some motivation on why you are here and
what are you preparing yourself for. But
before we look at what are the career
roles, let's also delve some time into
thinking what are the areas of research
in quantum computing and what are the
career roles in each of these areas. Of
course we'll talk about the India story
and the specific opportunities that uh
you know India has.
So going by the same road map idea on
the bottom I have the quantum hardware
and there are a number of research areas
for example quantum technologies like
you want to be able to have
superconducting as your quantum hardware
or uh photons or gold atoms or so on.
you want to be able to uh do material
discovery on what is the kind of
materials that I would use uh for
building my quantum hardware hardware
part and development. Uh there's a lot
of work in cryogenics in calibration in
control system chip design in semicond
fabrication and of course calibrating
these uh devices. Then there's a lot of
work in how can I build better
architecture for efficient and accurate
execution of quantum circuits on this
hardware. There's a uh uh if if you are
even a cloud expert right or you have
been doing things in cloud you can bring
that knowledge into quantum and uh you
know you can see how you can orchestrate
and uh in this in this uh space of
quantum supercomputing how your your
knowledge on cloud or AI will be very
very useful in orchestrating workloads
in able [clears throat] to do things
which you cannot do with any of these
systems alone be it quantum or classical
of course there's a ton of work to do in
terms of discovering new quantum
algorithms be it in the space of
simulation, optimization, quantum
machine learning or error correction and
so on. And then finally is the
applications. If you can build these
algorithms and how can you solve some
very important problems say in high
energy particle physics or in chemistry
or in biology or in finance or and and
so on. How can you uh you know start
from those complex problems in the in
the application space? What kind of
algorithms can you map there and how can
you get a benefit on those particular
applications is actually going to
determine your quantum advantage road map.
map.
So the research or the engineering roles
that we have here of course quantum
hardware engineer, microwave engineer,
cryogenic engineer or clean room
specialist and so on their list is
endless and there are very few people
who are skilled enough to do uh work in
quantum hardware not just in India but
across the world. This is a skill which
is very rare. There is more uh uh skills
or more uh availability of people in the
space of quantum software research. But
yes uh that is something that is also
more needed and there are a wide number
of places that if you are a quantum
software developer or quantum software
engineer or researcher you can actually
fit in yourself in in in all the layers
above uh the last layer. So there's a
lot of work in uh terms of quantum and
cloud researchers in the orchestrating
layer. Of course the algorithms uh
discovery needs a quantum research
scientist or an algorithm researcher
whatever is the name in different
industries they are different names but
yeah to me both these roles are encomp
encompassing the similar type of work
and finally uh a quantum scientist or a
quantum research scientist again you
know different names to the same
designation uh are the people who are
looking at these applications. There is
another business role which is more of
an industry consultant. Uh there is a
lot of roles in uh BD sales um even in
journalism about technical writing and
so on which cut across or cut through
all the layers and uh and are possible
uh or they they need a bit of technical
uh along with you know whatever other
role that they encompass.
Now of course as I said there are so
many other roles. There's a lot of
enablement required. So a lot of
teaching activities required. So there
would be need for more professors and uh
teachers in this space uh for client
support uh sales uh BDE for quantum
product manager uh and so on. So there's
a lot of such other roles which are um
which would need some amount of
technical knowledge but which are not
technically heavy or they are not
developers or research scientist roles.
Now coming to the India story. So as I
said India announced its national
quantum mission and there's a lot of
investment happening. So Indian
government has created four centers. So
the quantum computing center is an IC
Bangalore. The quantum sensing center is
an IIT Bombay. The quantum communication
center is IIT Madras and the quantum
materials and devices is by IIT Delhi.
And then there are a large number of
other institutions who are involved in
these particular technological hubs.
Right now each of these hubs will need a
lot of people be people like CEOs,
people like project manager, people like
researchers, developers, engineers and
so on. So there is a whole lot of need
or uh or or or roles that are opening up
across India and with Amraati and Andhra
Pradesh government h
with huge support India is going to get
its first quantum computer in less less
than 6 months and that's the reason why
we you know moved this course uh to
January and we opened this course now so
that people can be you know people can
do this course and then they can choose
the next set of courses that they want
to do
and and get ready uh to be able to run
utility scale or potentially quantum
advantage uh circuits on the physical
device when the device is here. Now this
and the quantum algorithm center across
the country these two open a lot of new
opportunities for India opportunities to
fund your own startups since governments
are giving a lot of grants and there's a
lot of VC funding flowing in and of
course in the existing startups there
will be a lot of positions if you see
200 plus startups will be supported so
each of these startups will need a lot
of people to run them then there are
positions opening in academia there are
positions opening in the industry there
are positions opening in the NQM hubs
and these pos and and and and so on.
This is a report by McKenzie where you
know they say that the percentage of
qualified quantum candidates is less
than 33%. So a significant portion of
this number of jobs that I showed you
are going to be uh you know unfilled
unfilled. So today in this era of
quantum utility, we need a focused
workforce capable of discovering which
problems in quantum computing are best
suited to solve. Quantum computational
scientists must be able to map these
problems to quantum circuits. And
tomorrow, as we realize our vision for
quantum supercomputing, we'll need a
much larger force capable of integrating
advanced middleware that orchestrates
cloud-based quantum and classical
computational resources. So
practitioners will need to be skilled in
these capabilities to address domain and
industry specific problems
and you know one another uh point that
people keep asking is PhD is mandatory
or not. So if you see PhD is green here
and for a lot of roles you know PhD is
not really required.
Uh so the prep for education or
experience for new hires it works very
well with bachelors or or even some some
amount of masters right but I the only
thing is that you should look at what
kind of roles you are looking at based
on your interest and advance yourself
and do such courses uh which you know
there are multiple providers like NPDL
who have such courses skill yourself
towards that role in the in the next few
uh months. months or or years
now in order to build this workforce
learning and enablement is key and
that's what we have been doing. So we
have been teaching a lot of classroom
courses. In fact uh in the last four
runs of NPTL this this particular course
we have taught around 37,000 students
and this course we have you know uh 50
more than 50,000 students and we have
been prioritizing investing resources
into advanced quantum education building
a diverse you know quantum road map. Now
uh you have a lot of active research
prototypes uh and collaborative skill
building. There are a lot of technical
working groups where researchers can be
part of. For example, the healthcare
life science working group which I am a
part of. Then there is a working group
in material sciences, high energy
physics, uh sustainability optimization
and so on. uh with NQM and AICT the
curriculum uh for quantum technologies
there's a BTE curriculum that was
announced and now there is a master's
curriculum that has also been announced
uh and there is a lot of faculty
development programs that are being run
across the country to be able to reach
that goal I've put in a QR code of the
IBM quantum learning platform uh the
platform is free all the courses are
freely available you can do the course
and you can go ahead and take the exam
to get a badge
uh if you want to and you know these are
all free so all the learning modules and
everything is to enable researchers to
advance their utility scale experience
explorations and you know how you can
help uh train your students to prepare
for this quantum workforce uh we have uh
John Watrus he's he's a he's a professor
and he has been running this um you know
quantum education course and uh there is
also a teach the teacher series that has
been planned and and and so on, right?
So there are multiple ways. So for
example, IBM quantum learning platform
that I showed you, we have typically
every year we have an annual coding
challenge which we call the quantum
challenge. There is Kiskuit global
summer school. Uh you can also take in
the Kiskit developer certification which
is a paid cert certification exam. uh
but there is you know a lot of free
content available on Kiskit YouTube
channel and of course you can become uh
Kiskit advocates if you have already
become a KisKit uh developer or
certified yourself with Kiskit developers.
developers.
So there are a bunch of series that keep
happening. So this is just for your
information. So I mean take this
opportunity skill yourself uh do
whatever it takes. But if you see that
the career opportunities there are a lot
of opportunities and there's very little
people who are trained in this realm. So
you know let's get skilled let's learn
more let's continue this passion of
learning and let's create the future of
quantum computing together. Thank you
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