[Music]
okay welcome back after the break
our next presentation will be on kisket
building a quantum community community
uh the talk will be given by dr anna fan
uh
and she is a researcher with a passion
for multidisciplinary science education
and outreach
at ibm quantum ana's mission is to drive
quantum education and quantum machine
learning for clients and grow the kisket
community in australia
there will be time for questions at the
end so please enter any
questions on the tab in venulis
thank you and over to you anna
hi everybody um good morning and i'm
talking to you all from melbourne i'm
really looking forward to giving you all
an introduction to quantum computing and
what we have ibm have been doing to
build and open source quantum computing
community based around our software that
we call kiskit
so ibm believes that quantum computing
will be a part of the future of
computing
there exists crucial problems of
business and societal interest which are
impossible to solve on any super
computer today or any one that we can
imagine building in the future
um
so if we think about it um to make an
analogy um think about driving a car
which is basically driving on a surface
that is two-dimensional
you can only go so fast and you for
example can't drive from new york to
london unless you've got a super fancy
you know
thing that can go across water
um but now consider air travel we've
taken something that is two-dimensional
to three dimensions with this extra
dimension this sort of extra degree of
freedom
we can do things which are just
impossible to do with land-based travel
that's sort of the analogy that we make
between
classical current computing and and
quantum
computing for level setting the
technology to make an analogy to
classical computing quantum computing is
sort of at the stage where classical
computing was in the 1940s hence i get
to show this this lovely image of the
the colossus
machine
and just as the decades after the 1940s
brought incredible
increases to power capacity and ease of
use to classical programmable computers
we really believe the next few years
will bring the similar advances to the
power and capacity of quantum computers
and hopefully much much faster as we can
really build up and use all of the
knowledge that we've learned in the past
decades in classical computing
so quantum computers promises solve a
variety of hard problems that classic
computers can't
it's it's not
often discussed but there are many
problems that classical computers simply
cannot solve because they are just too
complicated uh computationally difficult
these problems often have a compute
exponential character such that the time
or size of classical computer required
to solve the problem increases
exponentially with the size of the
problem
meaning that for any practical size
problem it is
impossible for classical computers to
solve it
universal
future quantum computers promise to
solve some of these problems
and moreover since quantum computing is
so nascent and newer technology we
simply don't know the full range of
possible problems a quantum computer can
solve
um as quantum computing advances we
anticipate discovering many new
applications so to make an analogy
against the sort of the history of
current computers we think about the
advent of of
gpus so graphical computational units
you know um a few years ago a couple of
decades ago they were purely used um
for what their name is you know
graphically
creating videos um and and games and
making that user experience
uh much more seamless to to churn
through those floating point
calculations
but over the past
decade we've really realized that gpus
can
those calculations can also be used in
in machine learning and artificial
intelligence
and that really wasn't a use for for
these
for this hardware when they it first
came out and we're
anticipating the same thing will happen
with with quantum computers
and i may end up dropping the term um
qpu every so often during the
presentation and that um
stands for a quantum
processing unit
so quantum computing is all about
learning how to use
quantum mechanical principles in a
completely new model of computation
um so i'm just going to go through a
little bit of this just to give you a
idea about how
computing using quantum mechanics is
feels different um to computing uh using
uh classical computers so
the first principle to get used to is
superposition so this is where a quantum
bit
or qubit in a perfectly deterministic
state um can still behave randomly so
what does this mean so if we look at
this these little diagrams um this top
one on the left with the the q0 and the
zero in those weird red brackets um if
we think about that as sort of a qubit
line
with time going from left to right
um
you start at the state zero that's what
sort of the zero in the in the little
brackets means and then we put on this
special gate
called a hadamard gate
and this gate puts this qubit into a 50
50 50 superposition between the states 0
and 1.
so if we continue to measure this qubit
over and over again uh 50 of the time
we'll get zero and fifty percent of time
will get one so an analogy to this is
like flipping a coin um you know fifty
percent of time you'll get heads fifty
percent of time you get tails but the
difference between a flipping the coin
exercise and this qubit is that if we
then take this qubit which is in the
superposition and put on another
hadamard gate afterwards
um because it's a deterministic state
um we actually end up putting that qubit
back into a zero state even if it was in
a one or a zero in that 50 50 50
beforehand
so this is sort of where it's different
from like classical probability where
you kind of can't undo that that coin
flip
so the other principle um
which is quite different from classical
computing is quantum entanglement so
qubits can be manipulated such that
their state cannot be described
independently of others
so an example here is where you've got
two qubits so you've got q0 and q1
you can put them in this this state
whereby
if you look at the results if you
measure qubit 0 you know that qubit 1 is
also going to be in the same state so
like if qubit 0 is in a zero state qubit
one is also going to be in zero state
while if it's in a one state um qubit
one is also going to be in a one state
and then on the um
bottom line i've just shown a different
example of another entanglement state
where if you know that one qubit is in
zero you know that the other qubit is in
one
and entanglement is a really interesting
principle because to mimic quantum
entanglement on a classical computer
requires exponential classical resources
and and quantum computation is really
the only way to access this unique
resource and this is important because
as we are trying to come up with
algorithms
to run on these quantum computers we
really want to take advantage of their
quantum mechanical properties so we
don't want to for example create an
algorithm which doesn't take advantage
of of entanglement at all
because then we could just
rewrite that algorithm to run on a
classical computer
so if we look at this table this sort of
demonstrates the potential
of quantum computing compared to
classical computing so
if we want to represent
a set of qubits in a fully entangled
state depending on the number of qubits
we're trying to represent here's a sort
of the the sort of canonical example of
how many bits it would take to represent
a fully entangled state so if we're
looking at two qubits um this will take
uh 512 bits assuming you know 50 64 bit
precision um
and then if we want to just add one more
qubit you need to double that number of
bits so three qubits will take um
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two to the uh ten so you know um
1024 bits if we're just looking at if we
keep doubling that for every bit that we
need to add um you quickly see that as
we're going to let's say 30 um qubits we
then end up needing you 17 gigabytes um
to represent a fully entangled state
across all of those
qubits and then even we go to sort of
numbers of just in the hundreds we end
up with more atoms on the planet um that
we would need to represent
that set of qubits
so what does this mean in terms of sort
of practical algorithms and practical
uses of these qubits
well one of the really interesting
applications and use cases for for
quantum computing is around trying to
understand the natural world better
trying to simulate um the interactions
of atoms and molecules with other other
molecules at a at a at a
more
granular scale to be able to understand
that interaction better and to to
be able to cr hope in the future create
more efficient materials
for things like solar power generation
or for catalyzing
um
fertilizers or for creating better
enzymes for you know carbon
sequestration new materials for you know
lighter more efficient batteries all
that all this sort of thing where
you really want to understand what is
going on at a very low chemical
reaction to sort of really improve the
efficiency of of those types of new
materials or those types of processes so
for example here we've got like a
different set of interesting molecules
and really comparing the number of
classical bits needed to fully simulate
what is going on in that molecule with
both the atoms and all of the electrons
in that molecule so if we look at water
right h2o um
that already would take about a you know
on the order of a thousand classical
bits and you can actually simulate that
using only 14 qubits so then if we jump
to something that i'm sure we all know a
lot about
caffeine
just just there that already is actually
um
really hard to stimulate perfectly
using classical computers so we would
need you know 10 to the 48 ish
um classical bits while people believe
would only need around 160 quantum bits
to to understand
that molecule so then we go on to
looking at you know the these sort of
biological molecules and and drugs you
can really see that scaling um
go exponential in the number of
classical bits that we would need while
we're still looking at qubits that may
be reasonable in in upcoming hardware
so a another example of a area of
interest for quantum computing is
quantum machine learning
um so one of the applications of machine
learning these days uh or in general is
actually like classification you know
given a set of data um you know
if we look at this set of dots you know
which ones are dark blue which ones are
light blue um you know which ones are
dogs which ones are cats um
whatever we want to the data could
represent uh people who usually want to
classify them into separate classes
so
for ease most classification algorithms
work linearly so you we want to find
some way to find
some
way of
visualizing that data
or
representing that data such that we can
find a straight line or a straight
hyperplane
if we're looking at multiple dimensions
to separate that data into the different
classes so if we look um just at these
light and blue dots
if we look at them in one dimension
there you can see there's no single
straight line that could separate them
but then if we sort of lift those that
data into two dimensions here a parabola
you can see that there is a nice sort of
straight line that could separate those
two classes
so the idea um in quantum machine
learning is to use that extra sort of
dimensionality
of the space that the qubits give you um
via entanglement
um to increase the dimensionality of the
dataset you're looking at
and i ideally identify that that
separating
hyperplane um
and this could possibly give you an
advantage compared to classical
computing if that um that dimensionality
calculation uh involves a lot of
entanglement
um so here's like a
interesting sort of example like
artificial data where we're looking at
these sort of
scattering of blue and red dots um if
you just look at them by eye in the the
left-hand side of that plot there's
really no obvious
classes or separating um
between the the red and the blue dots
but then on the other side is sort of
the the classes represented in a quantum
space um
so you can see how complicated that
quantum space could look like uh when
you sort of
merge it back to a two-dimensional space
that's sort of yeah the all i had on the
sort of motivation and use cases and
applications of quantum computing i'm
happy to take questions on that at the
end um but really to get to this space
um
what do we actually need to to sort of
build a quantum community and a kind of
workforce
um and sort of make it reality quantum
computing these use cases and
applications in the future so the we
like i really see the three pillars of
this is you know we need the hardware
you need you need um the
compute the quantum computers to
actually run these algorithms on um you
need the uh software and ideally that
software being open source so that um it
is they are available to everybody and
everybody can contribute to
the the base and then because it is such
a new technology um we really need a
education that is freely available and
um not just the
the uh cohort of people who
traditionally have learned about quantum
computing being sort of the the people
with physics backgrounds um
so yeah i'm going to go through sort of
each of these in in sequence um
starting with the hardware and i just i
just wanted to sort of put a little pin
in the sand that ibm has
um quantum computing devices which are
available on the cloud for anybody to
use you just need to log on and create
an account and you can you can play with
the five qubit
computer straight away
um so what does quantum computing
technology or hardware actually look
like or how does it actually work um if
we
think back historically about classical
bits um they came in in multiple
different
formats before getting to the the
silicon transistors of today you know
starting with electrical rear or
starting mechanically to be honest with
electrical relays and and vacuum cubes
to to now transistors
um
for a cubit we need some sort of a
quantum mechanical two-level system that
we can like manipulate to act as qubits
and then we need these qubits to be able
to talk to each other
um
so there are
several different um current like when
we're trying to think of
physical ways of realizing a qubit um we
see that there's sort of a three-way
trade-off between
how well we can control the qubits
versus how connected the qubits can be
to each other
and then how long the qubits can stay
in the states that we want them to stay
in so that we call that coherence
and so here's some just examples of
different
uh hardware uh technologies that people
have come up with to to
realize what a cubit could be
um from using photons to trapped ions
or sort of really interesting solid
state defects
at ibm and other companies
people are working on the
superconducting
uh circuits which i will go through in a
little bit more detail so
these superconducting circuits
are sort of printed on silicon wafers so
we can continue to use the fabrication
facilities and methods built up over
the decades
we're
creating the current
silicon hardware for classical computing
with some obvious differences
so first um to
make the qubits themselves um
we're looking at creating what is
essentially
a
lc circuit but it's a damped lc circuit
for those of you who know any sort of
classic i mean so electrical engineering
so if you if you're looking at a normal
inductor capacitor circuit like in the
bottom of the slide um the energy levels
of that
circuit are all the same
um so the difference between that and
the superconducting qubit circuit is
that um there is a non-linear
um
device in there
called josephus injunction
and that makes all the energy levels
different from each other
and that makes them addressable
so that means we can use sort of the
lowest energy state as our zero state
for the qubit and then the first energy
level as the um sort of the one state of
the qubit
and then you know we're looking at
approximately five gigahertz
energy difference between them meaning
they're around
240 ish milli kelvin so each of the
qubits will have a slightly different
energy level um between that that zero
state and the first state and that makes
makes them so that they can be
individual qubits and then we can
address them um using sort of microwave
uh input and output and readout so that
you know when we throw in a microwave
line of you know let's say 240 um
at that five gigahertz it knows exactly
which qubit we need to talk to
um
so um
i mentioned a very low temperature just
then 240 millikelvin and i've also
mentioned the word superconducting
which means that these things are quite
complicated in terms of the engineering
environment they sit in um there's these
chips with the superconducting qubits
and the microwave resonators used for um
talking the qubits to each other as well
as the microwave resonators used to talk
the input and output
um so these are
uh
put in the middle of a printed circuit
board and they need to run at
approximately 15 millikelvin um to get
that super conducting circuit to to
exhibit that um that energy differential
that we need um to you to use them as
qubits
um so these little chips sit at the
bottom of this super fancy fridge which
is called a dilution refrigerator um
uh where the top is sort of 40 40 kelvin
and then the bottom is at this um 15
millikelvin and then you can see all the
the super fancy sort of lie um
infrastructure there for readout
amplification
input and all that sort of thing and
then connected to that is a whole set of
outside the fridge
room temperature microwave electronics
and then of course
depending on the experimental setup you
know that can be connected to a internet
machine so then then
all of us outside that lab can then use
these devices
um
so then um just in in terms of evolution
the first qubit device that we put on
the cloud had five qubits um and you can
see sort of the the gradual increase of
this over time
um until sort of uh last year we
released a 127 qubit device which our
partners
and clients can use and we have a
roadmap sort of moving up from there to
a thousand qubits
by the end of of 2023 with the goal to
you know trying to get to some sort of
device that looks like it has more than
a million qubits also
in terms of space right um so this is
sort of the inside of one of those um
current dilution refrigerators you can
sort of hug it
but we're envisioning these like million
cubic devices to be in sort of this what
looks like a teleportation device that
someone can actually stand in sort of
two meters wide three meters tall
so it's it's very cool
um literally as well
uh so if we look at these devices
we're really thinking about um how many
qubits they have the quality of the
qubits in terms of the errors that they
have on them and it's important to also
think about the circuits that can run
per unit time on them um
in terms of sort of performance metrics
if we're you know wanting to compare the
different types of technologies that i
showed a little bit earlier
so that's all i had on the hardware
but now
moving on to the software so as well as
open access to
some of our devices online
we also have provided open source
software via our kisket framework this
is written in python which
not only allows developers access to
systems but provides the source code um
so that people don't have to learn like
a totally new different language um
and it also really helps sort of
integrate
current sort of workflows with possible
future quantum computing workflows
so what does programming a quantum
computer actually look like so classical
bits they're you know one
or zero or if i'd like to make an
analogy they can be like north or south
so if we look think about the state of a
qubit it can be an up it can be thought
of as an arbitrary point on the surface
of a sphere
so if we map that back to a bit let's
say zero is north and and say one is
south and that's all you can get to in a
classical bit
but on a quantum bit you can sort of
access all of the areas of that sphere
so you can sort of you know twiddle the
knobs to say that i want
my qubit to be pointing at melbourne
right rather than just zero north or
south
and so how do you get your qubit to do
that well classically if you wanted
something to be in zero or one you
create you know a set of one bit gates
um for that one bit or you know there's
a set of two bit gates for you know two
bits or multiple bits so that's the same
thing for quantum computing so um
obviously our one qubit gate's a little
bit more complicated because the idea is
to try to
move that cubit state sort of around
that surface of the sphere and similarly
we have two cubic gates which um where
the state of the qubit depends on this
um first qubit
um the biggest difference uh when we're
thinking about
uh these gates in quantum computing is
that because of quantum mechanics they
have to be reversible
um so this is quite a different way of
thinking about how to create these
circuits so they they're just being a
lot longer right so if you think about
and um
a sort of an or
circuit um you don't know like you know
if you have a one at the end of that or
circuit you don't know which bit was in
zero or which bit was in one you've
actually lost
um
that piece of information and it's not
reversible whereas all of the q
two qubit gates are actually reversible
just as a
side
so if you think about um
programming a quantum computer clearly
we don't program um computers these days
at the level of of of those electronic
gates um we've got low language
low-level programming languages as well
as high-level programming languages and
that's sort of where we want to get to
in in
quantum computing as well people have
been really thinking about what does a
quantum circuit look like uh what is the
intermediate representation between sort
of a high level language like python and
the low layer level
circuit language so there's multiple
different groups out there coming up
with different representations
um seeing if we can standardize them and
also putting them in practice to see if
they're actually
fit for purpose
as well
and so um kiskit and openchasm um which
is a intermediate representation is is
part of that so kisket like i mentioned
is an open source software development
kit for working with quantum computers
um at the level of sort of microwave
pulses as well as circuits
as well as high level
application modules so you can use
kisket to sort of run a quantum
machine learning algorithm as well as
create the microwave pulses you might
want to
you know if you want to play with what
um the qubits at the gate level
um
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and kisket is open source it has been
adopted um not by other hardware
platforms and companies
such as honeywell
aqt and inq to access their hardware as
well
so alongside the
sort of hardware robot which i showed
earlier in terms of trying to get to
um
this year releasing that 127 cubit
device and trying to get to the thousand
qubit devices in a couple years we also
have a
development roadmap whereby the idea is
to move from
users creating circuits to really think
about pre-built circuit libraries on top
of that with some pre-built quantum and
classical integration and being able to
run
um programs on
quantum computers in sort of dockerized
um containers
on virtual machines
and
really closely integrating the classical
computers and the quantum computers in
the future
and really making that development
frictionless
so
hopefully in the future when when people
are learning about how to program a
quantum computer we're not teaching
people about superposition and
entanglement and and circuits right from
the get-go obviously people want to know
because that's what's meant that what is
makes
classical computing and quantum
computing different um but it's not what
they need to
fully and deeply understand the maths
behind um
how a cubit is represented to actually
program it
which is sort of a little bit where like
we're really at their transition at the
moment in in making
um quantum computing really accessible
for those without strong
maths backgrounds
and that's really sort of the third
tranche of this this this this
three-legged stool um really educating
um and you know you can put hardware out
there and you can put software out there
but if nobody actually knows how to use
it or write it
that it's really kind of useless so
that's sort of the reason why the
education is so important to this um and
having a really friendly community where
people are willing to help others along
their journeys to quantum computing as
well so we've got a lot of a lot of
educational content out there um
from sort of
the our
online the our online composer and
quantum lab where you don't have to
install anything on your computer
anymore to um run and create your own
circuits and run them on a quantum
computer or write kisket code in an
online jupyter lab environment and and
send that to our devices we also have
um
and i've actually contributed to uh some
into our interactive
textbook
um which is we have one that is really
supposed to be sort of a guide to an
undergraduate or postgraduate
corner computing course but we've also
got
and we've been creating introductory
courses
we have a youtube channel where we have
series about you know um
live streams of public lectures for
people to keep up with the sort of
research level of quantum computing but
as well um as well a whole series uh
based on just learning how to program a
quantum computer you know booting up
kisket installing it how to what what
why do next
um
yeah and alongside that we also do
events we have documentation
um so i'm just going to flip through a
few examples of of those now so here is
sort of our introductory um online
introductory course you know it's a
three-level course
aimed at people from like technical as
well as non-technical backgrounds this
is um
something that we created after we
realized the textbook was a little too
um
too high level um well to assume too
much mass
background because this yeah
it's you know it's meant as
a now like obvious it's it's meant as it
says a universally quantum algorithms
it's course complement so it does assume
a lot of linear algebra um
so yeah separating out sort of that that
non-technical introductory course to
sort of a textbook was what we realized
was missing in in sort of our document
our educational tool moving forward
so the other things that we do is we run
events where we try to educate people
on how to use quantum computing so for
the past couple of years because of the
pandemic we've been running
global summer schools completely online
they're two weeks in the northern
hemisphere summer
in 2020 it was all about introduction to
quantum computing and quantum hardware
last year we ran a very very quick deep
dive into um conor machine learning um
this year um stay tuned um let's see
registrations should be open sort of
may-ish um and i can give you a little
bit of a hint on it will be in a
different topic from from the other two
so you deep dive into a different topic
then that was covered previously and all
of the lectures and labs from the
previous summer schools are available
online from kiskit.org as well um so
just some example some numbers from our
last summer school so we had uh 20 live
lectures and and five lab exercises that
people needed to complete to get us the
certificate
at the end of the summer school
um it was completely free and we had
five thousand um people register from
over a hundred countries um worldwide
and on top of that we also um trained up
about 100 mentors to help these
thousands of students get through that
course material it was really really
great because i was like sitting on that
discord um
the we actually had
people setting up
discords across different languages so
that people could talk to each other and
ask questions in the language that was
most comfortable for them to ask
questions in
so that was really nice to see that
people were getting the support in the
language that they were most
comfortable asking for support in they
always have to switch to
english as well as trying to learn
quantum computing at the same time
so if people don't have two-week
intensive set times to
go through a summer school we also run
multiple um
challenges across the year so these are
themed competitive programming
challenges um held multiple times a year
there's usually one in like may and one
in october novemberish um
sort of northern here northern
hemisphere wise
um
so here's sort of an example of last
year's one from the end of october um it
was all themed around sort of
applications of quantum computing um
so there was a set of challenges around
different applications of quantum
creating from finance simulation
machine learning and optimization and
they in um got harder and harder and
harder and so
we see the people who have just stepped
in um really being able to do that first
challenge which is um really based at
that beginner level um and then sort of
as they get more difficult we can see
people um
trying to complete or
um getting better at completing the
harder challenges and it's really nice
to see people coming back from previous
challenges and then and and going to
the next challenge and seeing that they
can progress further as their knowledge
of quantum computing um
in
evolves as well
so for example um yeah for this
particular challenge last october we had
about 3 000 registered applicants
um about 1 000 of which
submitted at least one exercise from
that first challenge
um and then around
600 or so
completed that final um or completed all
four challenges
so different there are obviously
different ways to contribute to the open
source community um there's the
obvious one of
code we've got three different sort of
github organizations kids get basic
physical community and kids get partners
where their partners are the ones that
um can use kids get to access different
types of hardware
um
and i really think a really nice one
that we have is translations um so you
can see that there are different
translation
group efforts going on to translate the
documentation and textbook into various
different languages some of these are
sort of ibm
they have ibm people sort of pushing
them um
so you have like a really great team in
japan who have been really working on on
translating that all but then some of
these groups are purely community run um
that thing like the indian languages
ones the tamil and the the bengali you
can see that they're almost 99 like
almost 100 fully translated into these
languages and that has been a pure
community
um effort and it's really great to see
people people doing that work and and
avoiding that access not just to the
physical devices but learning about
quantum computing as well
um so we also have a
slack
workspace where people can talk to each
other where people ask questions
as well as stack exchange and stack
overflow
um and if people
are really super interested in computing
contributing to the community we have an
advocate program which provides people
with extra mentorship and projects and
and networking and really being like
sort of not core
kids get developers um but really
connected to that internal team within
ibm um
and being able to to grow
that
um community and and being part of that
um group that really pushes where
the the future of the open source
project goes
alongside that um we've all uh to become
like a advocate one of the things that
you need to prove is that you know how
to program in kisket and one of the
things that we do have is a
certification um that opened up on last
year
so yeah that that's um something people
can do it's pretty exciting to like have
something to sort of aim towards if if
uh that's something that people are
interested in doing
so yeah um just to flip through sort of
some of the different things i've shown
here in terms of the community um
kiskit.org is is where it all starts um
from there you can join the kids get
slack you can get links to the the
textbook and the youtube um this uh it's
als following the twitter kids get
twitter is probably the best way to find
out about all the challenges and events
and the summer schools
and then if people um are super
interested and have contributed to the
community and have proven themselves to
be able to develop in kiskit they can
become a kid's good advocate
and thank you and i think i've left like
five ish minutes to answer any questions
that came up during the talk
okay thank you anna for such a wonderful
introduction to a new world for many
including me
yes indeed we have a few questions uh
four questions have come in on the chat
uh so i can give you the first one uh is
quantum machine learning only useful for
working on problems with quantum
behavior would you ever use it for
classical problems
that's actually one of the ques like
that that is actually the key question
in
the research the community around
economy machine learning at the moment
so people have shown advantage in using
quantum machine learning for quantum ish
type data
and lots of people are looking at using
um quantum machine learning for
classical types of data sets um
and
it's still an open question and a very
interesting open question at that
there's a couple of interesting papers
i believe that are online so
where people have used quantum machine
learning to look at
electronic health records
um or or data of the large hadron
collider
in terms of particle physics data uh but
yeah it's it's
yeah open question
okay great uh next question uh what
microwave frequencies are used uh we'd
like to see more of the room temperature
signal chain
ah sure so i actually don't know um too
much about
the
hardware at that level um i've really
reached into the the quantum computing
as sort of on that
kisket level side so that is something i
would have to look up um and maybe you
could the person who's asking the
question could probably find that
information better than me so i know we
have a lot of um information out there
around sort of
that on the ibm.com quantum computing
website as well as various papers i yeah
that's really not my area of
speciality
okay the third question uh is there
research being undertaken to discover
the social questions and challenges
these new technologies may pose to each
of us
yes there's actually a group
um looking at sort of the ethical um
consequences of quantum computing
um and yeah the societal impacts of um
such here in australia um i think
they're based
out
of queensland
so yeah people are definitely looking
into that and groups in australia as
well
great
uh we'd like to connect with the
bengali community on quantum computing
contribute what needs to be done
sure um so the best way to connect with
the translation communities um on
if you join the kisket slack workspace
there's a channel for kids get
localization which is where all the
discussion around the translations
happen
i'm hoping that that makes sense enough
for the question
questioner
okay and the final question for today
uh does reversibility of quantum
functions make it difficult to implement
algorithms that discard information
um
it does okay
it doesn't it's just more a different
way of thinking about how you need to
compute so it's more thinking that um
you know when you're creating an adder
usually when you do that classically
you need
a certain number of bits if you're then
trying to do the same thing quantum
mechanically you may just need more
qubits to sort of
um
think about
building that algorithm up from the
circuit level and that that's
also another way of thinking about it
because of the difference between
quantum computing and classical
computing it doesn't make sense to go
okay i can do something in classical
computing i'm just going to do the same
thing in quantum computing it's really
thinking about well these are the things
that make quantum computing different
um
how do i
write an algorithm
um exploiting these differences
um i know that's sort of a
not the direct answer to the question um
but these the algorithms can be written
um that reversibility is more of a
constraint on how you're writing
um
the the algorithms themselves so you can
discard information
by not looking at it again um
it just means the way you're writing
that algorithm is going to be different
okay thank you and it seems that the
answer to all the other questions is to
go to kisket.org yes exactly okay thank
you very much for that presentation and
that's it until our lunch break now and
we have another session at 1 30 in this
room
thank you all