The ARC Centre of Excellence for Quantum Computation and Communication Technology (CQC²T) is focused on delivering world-leading quantum research to develop full-scale quantum systems – encompassing ultra-fast quantum computation, secure quantum communication and distributed quantum information processing.
UTS QIS joined CQC²T in 2017 as a collaborating partner led by Chief Investigator Professor Michael Bremner. Prof Bremner’s research at CQC²T is focussed on methodologies and capabilities of quantum software and information technologies, with a significant focus on the development of new applications for these technologies.
For more information on the partnership: The Quantum Algorithms and Complexity Program
00:06
my name's Hyman Devitt I'm a founder of
00:10
a quantum consultancy company called
00:11
h-bar I'm also the chief science officer
00:13
of a new startup called Turing and I'm
00:17
delighted to be asked to chair this
00:19
panel on the quantum applications stack
00:21
and how it might influence development
00:24
within the private sector and
00:25
revolutionized the world so first of all
00:28
we do have representatives from both the
00:31
startup community more established
00:33
corporate representatives and from the
00:36
academic community and I thought
00:37
initially I just run down the panel and
00:39
let everyone do a minute or so
00:41
introducing themselves and what their
00:44
primary focus is so I suppose we'll
00:46
start with Martin down the end from
00:47
Microsoft thanks Simon a Martin role or
00:50
with Microsoft Research let me just
00:52
begin by saying it's a very exciting
00:54
time to be working in quantum computing
00:56
you see a lot of emerging devices we've
00:58
just heard about a Google effort and
01:00
several other kind of companies and
01:02
other outfits that try to build a
01:04
quantum computer
01:04
some of them allow you to actually play
01:06
with it I personally work on on the
01:08
algorithms side I try to find problems
01:10
where it can wait we can have it and a
01:12
tremendous speed-up over what you can do
01:14
classically of course historically the
01:17
first such problem was factoring which
01:18
has implications in security and privacy
01:20
but we're going to move beyond that and
01:23
find trying to find problems that are
01:24
more of an immediate business value you
01:27
heard by in Alan's talk about chemistry
01:29
we have big chemistry effort going on at
01:32
redmond we have matthias Troyer just
01:34
recently joined our group he's one of
01:35
the leading a classical experts on the
01:37
topic we have material science
01:39
applications we are looking into machine
01:41
learning applications that's kind of a
01:43
new area and the applications are not as
01:46
clearly defined on my like personally
01:49
I'm interested in costing algorithms so
01:51
you want to kind of at this point it's
01:53
everything is unclear what's the final
01:55
gate said what we can actually support
01:57
but you can still even at this point
01:59
make some statements about how big the
02:01
machine needs to be roughly to run some
02:03
applications I mean I'm interested in
02:06
that try to build the software that
02:08
allows us to do it and we kind of cost
02:10
out the number of gates the number of
02:12
queue
02:12
it's that that's kind of part of the
02:14
Microsoft approach to what's going on
02:16
computing great thanks mom Matt from
02:19
juicy we're sure my name is Matt Johnson
02:20
I'm the CEO of QC where we are a quantum
02:24
computing software company based in Palo
02:27
Alto California I guess the the main
02:30
points to think about with us is first
02:31
of all we're focused on applications and
02:34
the application sets we care about our
02:36
quantum machine learning finance and
02:39
search and search for us in the broadest
02:42
sense of the word means finding a needle
02:44
in the haystack for graph based or
02:47
network based problems another
02:50
characteristic of our firm is that we're
02:52
specifically focused at enterprise
02:54
problems and making sure that the
02:56
software we're developing is reachable
02:59
and usable for novice enterprise users
03:01
so the composition of our team reflects
03:04
that we're computer scientists quantum
03:07
algorithm people and and physicists so
03:12
we work together as a team to build the
03:14
technology stack that sits on top of the
03:16
bare metal or the API that the hardware
03:18
vendor will expose up through the
03:21
application side all right thanks man
03:23
Michael from Q branch hi good afternoon
03:26
my name is Michael Brett I'm the CEO of
03:28
a company called Q branch we're
03:31
headquartered in Washington DC and have
03:33
a development team based in Australia
03:36
where a data analytics company the bulk
03:39
of our business is focused in applying
03:41
machine learning and data science to
03:43
challenging enterprise problems but we
03:46
see quantum computing as a long-term
03:48
strategic differentiator that we want to
03:50
be involved in so we've been investing
03:53
some of our equity and some of our our
03:55
partnerships into understanding the
03:57
quantum computing landscape that sort of
04:00
applications that might be enabled by
04:02
quantum computing that would have an
04:03
impact on our existing business and
04:05
hopefully those two business areas over
04:07
time will merge together and we'll
04:09
introduce our our classical analytics
04:11
customers to quantum and our quantum
04:13
customers to the analytics work that we
04:15
do on the classical side it's a pleasure
04:18
to be here thanks for the invite great
04:20
things Michael and Gabriel from
04:21
Volkswagen hi everybody i'm gabriela a
04:24
composer
04:25
from the Volkswagen data lab here in
04:27
Munich forks wagon meaning one of those
04:29
companies that we heard is going to be
04:31
disrupted by new technologies so the
04:36
data lab here for which I work for is
04:38
basically a center of excellence for
04:40
everything related to data analytics of
04:42
dense analytics within the company
04:44
Volkswagen that means we work with all
04:46
the group brands that are owned under
04:49
the umbrella companies of the Volkswagen
04:51
Group our idea is to bring together
04:53
business experts data analysts
04:56
technology experts and try to build data
04:58
driven innovation for the company as
05:00
part of this effort we focus on
05:02
different areas like again big data
05:05
analytics machine learning deep learning
05:06
but we also have some research topics
05:09
like that go into the direction for
05:11
example of natural language processing
05:13
and chat bottle iPad one of the research
05:16
topics we are working on is also quantum
05:18
computing to try to prepare us for the
05:20
new revolutions coming and the new
05:23
products that we will be able to create
05:25
with that so that's why I'm here and
05:27
thanks for the invitation great thank
05:29
you and finally Michael Bremner from UTS
05:31
in Sydney and the CQC T now that's right
05:35
so as you've just said I work for two
05:38
research centres both based in Australia
05:41
I work at the University of Technology
05:43
Sydney --zz Center for quantum software
05:44
and information which is a Research
05:47
Center which is focused on the
05:49
theoretical aspects of quantum computing
05:51
so that goes everywhere from blue sky
05:53
research into the mathematics underlying
05:55
quantum algorithms all the way through
05:57
to working with teams for
05:59
experimentalists on developing
06:02
benchmarking and applications for their
06:03
devices I do that in part with the
06:06
Center for quantum computer technology
06:08
the quantum computing and communication
06:11
technology which is an Australian
06:13
government funded research center great
06:17
so what I thought I'd do is try to
06:19
tailor off from what Alan did but try
06:22
and give it a little more details so
06:25
first of all I'd like to get Mick and
06:27
maybe Martin might want to jump in on
06:28
this to sort of give us an outline of
06:31
sort of the the quantum algorithm and
06:33
the quantum application landscape
06:34
because we've talked with hope from Alan
06:36
there's two very important regime
06:39
here there are things that non error
06:41
corrected quantum algorithms and what
06:42
can be done there all the way up until
06:45
fully quantum error corrected Universal
06:46
algorithms which as was mentioned is
06:49
going to take some time so make if you
06:52
want to talk about sort of this area a
06:54
little bit and then Martin might want to
06:55
jump into some of the work that
06:56
Microsoft's done to try and find few
06:59
qubit applications and try to get around
07:02
not requiring error correction okay so
07:06
that's gonna give me the whole history
07:07
of quantum computing in thirty seconds
07:09
but all right so I guess it's important
07:13
to understand the quantum algorithms
07:16
development has come has been going on
07:18
now for since the early 90s
07:21
and to begin with it was really a
07:24
mathematical topic it's a topic of like
07:26
studying these mathematical structures
07:28
and it was done very hypothetically yeah
07:33
imagine if you had this thing called a
07:34
quantum computer which maybe one day
07:36
someone develops and people focus on
07:39
developing algorithms and for universal
07:40
quantum computers of work you know
07:42
ideally with error correction and
07:43
everything like that in recent years
07:46
this is really shifted and I think
07:47
Marcus also played a big role in this as
07:49
has Google and a few others too as
07:52
Hardware has got like much better to the
07:56
point where we're now actually you know
07:57
talking about building really
07:59
interesting devices we've focused a bit
08:03
more on near-term applications because
08:05
now we can actually see the thing we
08:06
need to develop forward we can talk
08:07
about what is the fidelity what is the
08:09
architecture what's it going to look
08:10
like we're now developing applications
08:13
much more specifically suited to these
08:14
sort of tasks now as was alluded to by
08:18
Allen like we're approaching this regime
08:21
which people call sort of those raging
08:23
with quantum computational supremacy
08:24
which is its it can be a sort of vaguely
08:28
defined term which means we're heading
08:31
towards building devices which in
08:34
principle could have the capability to
08:36
for very specific applications I'll
08:39
perform every classical quantum computer
08:42
sorry classical computer to begin with
08:45
these devices these light applications
08:47
are very
08:48
we were put it and the way that they've
08:49
been developed has been to you know find
08:53
some mathematical leverage which you
08:55
know lies down in this you know the deep
08:57
sort of structure of some problem and
08:59
then build that up to talk about an
09:04
actual sort of application or a
09:05
benchmarking tasks that a device can do
09:07
which is quite different from the sort
09:10
of traditional way of doing development
09:12
for enterprise in an industry very much
09:16
agree with those points at Microsoft you
09:18
of course observing very closely the
09:20
developments around quantum
09:22
computational supremacy which means kind
09:25
of you tried to separate the classical
09:27
world from the quantum world and find a
09:29
problem that you cannot solve on a
09:30
classical machine period so the trouble
09:33
with it in a sense is that none of these
09:35
problems are really seem really useful
09:36
was we of course want to set our sights
09:38
to problems that we care about the
09:41
hardest problems people care about and
09:43
we cannot solve them in any reasonable
09:45
amount of time even if we cover the
09:47
entire planet with computers we could
09:49
not solve them and one area where we
09:51
found such problems are in computational
09:53
chemistry so understanding chemical
09:55
reactions it's very important we heard
09:57
in in Alan's keynote about the problem
10:00
of nitrogen fixation for instance it's
10:02
done currently like harvesting nitrogen
10:05
from the from the air it's done
10:06
currently with a very inefficient
10:08
efficient process called the
10:09
abba-baba-baba process which is like
10:13
happens under high pressures high
10:15
temperatures and you need catalysts for
10:17
this and has a very relatively lowly low
10:20
yield so a quantum computer could help
10:23
us it has been shown on on a piece of
10:25
paper that it could could help us kind
10:27
of innovate there and find better
10:30
catalysts that help us having higher
10:31
yield for the process of course now it
10:34
kind of you have to ask several
10:36
questions first of all how large has a
10:38
quantum computer does it have to be to
10:40
actually implement this like how many
10:42
qubits how many operations will you have
10:44
to do and kind of can you really solve
10:47
the optimization problem of finding a
10:48
catalyst and then implementing all these
10:50
things are kind of we have done some
10:53
baby steps towards that we cost that out
10:55
initially that you will need around 100
10:57
logical qubits
10:59
to understand the process of nitrogen
11:01
fixation the way nature does it using a
11:04
molecule called nitrogen ease and nobody
11:07
really understands how nature does it
11:08
but having a quantum computer we could
11:10
at least kind of have a chance of
11:13
understanding it and there it's maybe
11:15
interesting for the late for the lay
11:17
persons to know like one of the key
11:19
applications is just simulating physics
11:21
so we try to build this machine this
11:23
incredibly contrived machine and one of
11:26
the key application is just simulating
11:27
other physical systems and that's one of
11:29
the killer applications presumably is
11:31
just to do that
11:33
if we have such a machine we can apply
11:36
to problems and material science maybe
11:38
find better ways to construct
11:39
superconducting materials that would
11:41
super conduct at higher temperatures
11:43
that would be a really really big deal
11:45
breaker we could we could hopefully have
11:48
kind of the catalysts I mentioned in
11:50
machine learning would we could
11:52
hopefully use a quantum computer to
11:54
train models in a better way there early
11:57
indications that that might be possible
11:59
when you look at deep networks you can
12:01
model it by a Boltzmann machines and
12:03
it's known that a quantum computer can
12:04
help train Boltzmann machines in a
12:06
better way so better is kind of tricky
12:08
to define in that context it means
12:10
specifically that a method that it's
12:12
classically used based on stochastic
12:14
gradients can be improved on by having a
12:17
better quality gradient in a like if you
12:19
have a computer you can have the kind of
12:21
you can point in the right direction
12:22
make a step so in the in the gradient so
12:24
a few very domain-specific applications
12:26
that's kind I think one of the keywords
12:28
that you said in there is when quantum
12:30
chemistry you're talking about a hundred
12:31
logical qubits error correction is still
12:34
going to be required at this level so I
12:36
mean if I can talk to Gabriel about
12:38
certainly the interest of these bigger
12:40
companies such as Volkswagen I mean
12:41
where do they come in on this in regards
12:44
to what they're looking for are they
12:45
hoping that there are going to be
12:47
applications out there at this sort of
12:49
50 qubit to a hundred qubit level not
12:52
logical qubit qubit level well I think
12:56
we are taking the proactive approach
12:59
that Alan mentioned before so we are
13:01
trying to see what's already out there
13:03
and how we could use it for realistic
13:05
problems and we had an experience just
13:08
this year a few months ago we were able
13:12
to try
13:12
our first let's a quantum application I
13:16
didn't have I don't know if I qualify as
13:19
a quantum developer so I didn't have the
13:20
gut to stand up before but basically
13:22
that's what we did we had a partnership
13:25
together with our colleagues at d-wave
13:27
and we tried to set test their systems
13:28
so not at 100 cubits scale but a
13:30
thousand to 2,000 and what we did was
13:34
with a small team locked in a room for
13:37
two months and the Hut without pizza -
13:41
we tried to build something useful out
13:43
of it so we tried to to ask ourselves ok
13:46
so we have a real quantum computer here
13:48
that is available this is the top of
13:49
tech the technology that we have today
13:51
so can we use it to solve a real problem
13:54
that matters for our industries
13:56
something along the lines that was
13:57
discussed before and the things that the
13:59
thing that we did was we created a small
14:02
application that ran on the d-wave and
14:04
that could optimize the traffic flow so
14:07
optimal routing for many cars on the
14:10
street we had some very interesting
14:13
results that represented at cbiit this
14:15
year we are still working on that but
14:17
the outcome of this exercise was that
14:19
yes there is something that we can
14:20
already do there is something that
14:22
already works with the current quantum
14:24
technology of course the limiter is a is
14:27
the scale at which we can push this
14:29
problem we are now able sorry to let say
14:33
perform this optimization in a few
14:35
seconds so from 5 to 15 for a few
14:39
hundred cars on the street and we can
14:41
select between multiple possible routes
14:44
the optimal route for each and every car
14:47
based on the behavior of all the other
14:49
cars so yeah so this is for example one
14:52
of the possibilities of some new let's
14:55
say applications that are not common in
14:57
our industry yet that could be
14:59
differentiators for our company we are
15:02
still of course exploring other possible
15:04
applications that are related to
15:06
optimization in many other aspects of
15:09
the things that we do I mean in the case
15:11
of how how Volkswagen look at this this
15:13
problem itself is it do you reach out to
15:16
the quantum companies and say hey we're
15:17
interested what do you suggest or do you
15:20
have your own in-house teams who have
15:22
some understanding of some expertise
15:24
just
15:25
then go this might work for us and so
15:28
we'll reach it right so of course we are
15:31
not able to do this all ourselves we
15:34
have worked in a close partnership with
15:36
d-wave to perform this exercise we are
15:39
also gaining some some education
15:41
ourselves I'm a physicist as background
15:43
so I sort of understand some of the
15:47
technical problems not all of course but
15:48
and my colleagues too are trying to
15:51
learn more but let's say we are we are
15:54
going through two different paths one is
15:57
to be proactive again with respect to
15:59
the evolution of the technology the
16:01
other one is trying to build within the
16:02
company already the competences that
16:04
will be needed
16:06
once quantum computing becomes really
16:09
commercially available and more spread
16:12
as it is right now yeah that's something
16:14
that I've noticed quite a bit is that a
16:16
lot of these companies are looking to
16:17
develop competency within this area so
16:19
Matt and Michael would feel free to sort
16:22
of jump in on each other you guys your
16:25
company's sort of work in this software
16:26
space it's sort of these these non error
16:28
corrected levels to try and consult with
16:32
people or develop these applications
16:34
with with various stakeholders so how do
16:37
you guys tackle I mean you both work
16:39
with financial sectors a little bit as
16:43
well so how do you approach that again
16:46
do they come to you or do you go to them
16:48
yeah so building on like on the
16:51
experience from Volkswagen that that is
16:54
the style of project that we typically
16:56
get engaged in where a company sees
16:59
quantum computing on the horizon that's
17:02
coming up towards them they're picking
17:03
up signals like Google are putting out
17:05
today that hardware will soon be ready
17:07
in a tractable timeframe to start to
17:10
think about this technology and so
17:12
organizations are seeking to understand
17:15
what the impact is going to be on their
17:16
business and one of the troubles with
17:18
quantum computing is that the specific
17:20
application in that business is
17:22
non-intuitive and non-obvious in an
17:26
initial meeting so you you need to spend
17:28
some time with that company to
17:29
understand their particular processes
17:32
where they're spending a lot of
17:33
computational power already or what are
17:35
the problems that they're
17:37
avoiding because it's too
17:38
computationally intensive and how
17:40
quantum computing might be able to have
17:42
some impact there I think it's important
17:44
to consider the importance of emulators
17:47
of quantum computers so these are
17:49
systems that run on classical machines
17:52
but emulate the behavior of a quantum
17:54
computer at least to some level of
17:56
fidelity into some number of qubits and
17:58
that provides a inexpensive accessible
18:01
way for an enterprise to start to
18:04
develop those capabilities and skills
18:05
and build the awareness of quantum
18:07
computing and demystify ER in in a
18:10
practical way so getting hands-on with
18:12
an emulator is something that can be
18:15
done ahead of the hardware development
18:17
and then once you've got applications in
18:19
mind then test those on the hardware as
18:21
and when it becomes available so I agree
18:25
with what Michael said I would add a
18:27
couple of points and I think this
18:29
applies to any member of the quantum
18:31
computing software ecosystem the most
18:34
important thing to do is to be working
18:38
very closely with enterprises regardless
18:40
of the sector they're in and and there's
18:43
this amount of creativity that's even
18:46
more important than technical knowledge
18:47
of the hardware topology or any other
18:50
aspect of quantum computing and it's
18:53
that ability to map the problem the
18:55
enterprise has and and to have a quantum
18:58
representation of that to enable it to
19:00
be run on a quantum processor and that
19:02
sounds like a very technical thing to do
19:04
and it is but it's actually it requires
19:07
an equal measure of creativity and so
19:10
when we I guess tactically think is a
19:11
company where we want to focus our time
19:14
clearly there's a better scientific
19:17
argument to say let's focus on quantum
19:20
chemistry quantum quantum materials
19:23
quantum quantum physics because those
19:25
datasets are already in a quantum state
19:27
and they can run kind of natively on a
19:29
quantum processor but we're actually
19:31
taking a bat a very clear bet that the
19:34
hardware vendors the ones that are in
19:36
this room so that's IBM Google d-wave
19:39
Righetti we're taking a bet that the
19:41
engineers there are going to be able to
19:43
pull forward useable early generation
19:46
quantum processors in a fairly short
19:48
period of time
19:49
so we are not just doing that kind of
19:52
application set but finance working with
19:56
the Shah one of our investors working
19:59
with Airbus on engineering design and
20:02
test problems though those are areas
20:04
that may be somewhat under addressed
20:06
right now but we're taking the bet that
20:08
quantum processors are going to be more
20:10
powerful sooner than the sort of the
20:14
conventional wisdom suggests they are
20:15
and so that's really where we want to
20:17
put our footprint down so I mean how do
20:19
you I mean a few qubit quantum
20:22
algorithms that have a marketable impact
20:24
something that that is demonstrably
20:25
better than what we can do classically
20:27
has an application that can be marketed
20:29
or commercialized I mean this has been
20:31
the holy grail of the academic community
20:33
to a certain extent for the last twenty
20:34
years and still really doesn't exist yet
20:37
so Mike what you talked about emulators
20:39
now I mean emulators only take you so
20:42
far
20:43
arguably you can do a lot of it on pen
20:45
and paper once you get to twenty thirty
20:47
cubits and then when you're pushing past
20:49
thirty cubits you find your classical
20:50
computer can't keep up so within what
20:53
you see wearing q-branch are doing I
20:55
mean how much of it is app development
20:57
at this sort of non error corrected
21:00
level so we're going to have a really
21:02
tough time as a community if we can't
21:05
get quantum programming into the hands
21:08
of classical computer scientists and
21:10
software engineers and to make it normal
21:12
for graduate from computer science from
21:14
Stanford who's never thought about a
21:16
quantum computer before to make it
21:18
normal for that computer scientist to
21:20
pick up a library and start to use it
21:22
and think about it as a classical
21:24
problem so that means building out the
21:26
libraries and the toolkits and the
21:28
emulators that exist within a normal
21:30
development environment that can run on
21:32
premises at their location whether it's
21:34
at a financial institution or university
21:37
or wherever to get access to the data
21:38
sets that they have and so for for our
21:42
company at least the way we think about
21:43
our role here is to do kind of a
21:46
top-down work so to look at the
21:48
applications that might be relevant and
21:50
to drill down words and building out
21:53
those libraries and compilers and
21:54
toolkits that go into the hands of
21:56
regular developers well the hardware
21:58
teams and the people close to them do
21:59
the bottom-up build the bare metal the
22:02
all systems and the the abstraction
22:04
layers on top of that and so building
22:08
out that workforce that has some
22:10
understanding of quantum computing and
22:12
can use those libraries that is one of
22:14
the enablers to making sure we can find
22:16
the applications down the track and just
22:19
really quickly to add to that you
22:21
brought up a very interesting point you
22:22
talked about for circuit model machines
22:24
at least let's talk about adiabatic or
22:26
annealing processors in a second you
22:29
talked about the lack of development so
22:32
to speak it's an odd thing to do so as
22:35
Michael Wright and and certainly there
22:38
are you know six or seven quantum
22:40
algorithms that have provable end to end
22:43
speed-up potential the issue is those
22:46
algorithms were developed in the
22:48
scientific and research community and
22:50
they really weren't expanded or put to
22:52
put to the test with real data sets so I
22:54
think the interesting challenge will be
22:55
to take these algo primitives that are
22:57
out there and to mash them up against
23:00
enterprise problems and to force the
23:02
algo community the practical algo
23:05
community to try to shoehorn those real
23:08
problems onto those algo primitives I
23:10
mean I mean those algorithms that do
23:12
have provable speed-up unfortunately do
23:14
require extensive error correction to
23:16
implement sorry you want to jump in yeah
23:23
I'm just going to jump in and say I mean
23:27
it's really important to understand that
23:30
quantum computing speed-up is it's not
23:33
just a matter of which I think everyone
23:35
here agrees with but it it's important
23:37
people understand it's not just a matter
23:38
of something going faster so it's really
23:41
hard to map problems into a subroutine
23:45
which is you know will deliver you a
23:48
speed-up and and it really deeply
23:50
depends on in some ways the architecture
23:52
the way in which especially we're
23:55
talking about near-term devices like not
23:57
everything's going to work perfectly and
23:59
sometimes those imperfections can
24:03
swallow your speed up completely or it
24:05
turns out that you know a classical
24:07
computer can do just as good a job this
24:09
is a thing which I think everyone the
24:11
stage here is
24:12
is kind of focused on trying to get to
24:14
the bottom of from different
24:15
perspectives I think it's interesting
24:17
that people looking at sort of
24:19
enterprise development and how do we map
24:21
those problems onto the existing
24:24
subroutines and from an academic
24:26
perspective there's really a little work
24:28
on finding those points to differ a
24:30
differentiation between classical and
24:32
quantum computers and trying to leverage
24:34
that out I think that's yeah it's a bit
24:38
of a summary of what I think is going on
24:40
I mean it is the interesting approach of
24:42
Microsoft is the Marten you want to
24:44
gonna have to say it real quick because
24:45
we're getting short on time in kind of
24:48
this annyoing so the this method that
24:50
people might have heard of called
24:51
topological quantum computing there's a
24:53
bit of a distinction there are
24:54
topological quantum error correction
24:56
codes which work on qubits
24:58
but what Microsoft's approach is is
24:59
these systems with intrinsic topological
25:02
protection and it's a bit of a gamble
25:04
because it would in one sense
25:05
mitigate the error correction
25:07
requirements but it's also based on
25:09
particles that don't really exist yet
25:12
right
25:13
yeah okay let me try to set it in a few
25:15
remaining minutes so for the problem of
25:19
decoherence has already been mentioned
25:20
it's a very tough problem that the
25:21
corner computer has to deal with
25:23
intuitively it's like kind of like a
25:25
negative interest rate like if you put
25:27
money in the bank and after a year you
25:29
have instead of hundred dollars you have
25:30
99 dollars and then it keeps compounding
25:32
the negative interest rate right and you
25:34
can see that quickly that that kills the
25:36
computer if you have a number of steps
25:37
you want to do it just drops
25:39
exponentially the amount of signal you
25:41
get from the machine and it makes it
25:43
hard to scale up a quantum computers as
25:45
simple as that so there several ways out
25:47
of that we could just go bare and work
25:49
with the pair qubits and hope that our
25:51
circuits never have to be very deep and
25:53
that might work with the great proposals
25:56
on quantum variation eigen solvers that
25:58
actually might give you a good good
26:00
answers the other approach is to add
26:01
error correction which comes with
26:03
overhead so you have something like
26:04
classically of redundancy triple
26:06
redundancy yourself stuff like that we
26:08
have that in the quantum world - but our
26:10
factors are larger they're not three
26:11
there may be seven and the most
26:13
optimistic case may be 10 to the 4 in a
26:15
more pessimistic cave so Microsoft has
26:17
set it aside on a particular approach
26:20
when we try to build a sort of a
26:22
self-correcting memory so classically
26:24
you know like hard disks
26:25
for instance they're pretty resilient
26:27
right if one of the magnetic magnetic
26:29
spins flips it flips back because the
26:31
neighboring spins just force it back so
26:34
there are similar things in the
26:36
topological insulators so structures
26:38
that have topological materials
26:40
semiconductors topological materials in
26:42
fractional quantum Hall effect
26:45
they have been observed they show
26:47
extreme resilience against actions that
26:51
the environment might do to your system
26:52
so in a sense it's very protected
26:55
against the environment on the flip side
26:58
it actually doesn't want to want you to
27:00
talk to the system anymore so the only
27:02
thing you can really do is some
27:03
topological moves you can braid it
27:05
around each other and hopefully by doing
27:07
so you can do operations so to answer
27:10
the question we have set our side on
27:12
that particular approach to build a
27:13
topological quantum computer the
27:15
experiments running right now in
27:17
Copenhagen and Delft the two
27:18
experimental groups that are now part of
27:20
Microsoft they try hard to build a small
27:22
computer at this point once we go beyond
27:25
a two qubit gate which will be a crucial
27:27
step in that roadmap there there seems
27:30
to be no limit like for a physics point
27:32
of view there's nothing that on the
27:34
theory side prevents us to build a
27:36
large-scale computer that needs only
27:37
very little error correction and then
27:40
when we when we scale up that machine we
27:42
could reach practically quantum computer
27:44
quite quite quickly and have to worry
27:46
about actually compiling into that that
27:48
architecture so that's kind of
27:50
engineering challenges and it's chic and
27:52
very for Microsoft Challenge sure so I
27:55
suppose that's what we really have time
27:57
for on this so I've got a couple of
27:59
minutes for questions if people want to
28:00
throw their hands up before somebody
28:03
cuts me off so yeah I've heard it said a
28:09
number of times that the basis for
28:11
Microsoft's quantum computers these
28:13
quasiparticles haven't been proven to
28:16
exist and yet I've also heard it be said
28:18
that there is no explanation other than
28:20
these particles exist for why these
28:23
machines are doing what's happening
28:25
right now so I was wondering if we can
28:27
sort of officially get to the resolution
28:30
of this question right here today is it
28:33
true that the Microsoft machine
28:35
essentially utilizes an effect that
28:37
there is no
28:38
explanation other than you have these
28:40
quality particles he seems like it it
28:42
was first observed in 2012 by Leo
28:44
københavn and in Delft back then there
28:46
was a big debate and there were several
28:48
people the papers written that had stuff
28:49
like smoking gun evidence yes or no in
28:52
the title I think by now there's so many
28:54
signatures of this these so-called
28:56
Mariana the remotes that have been
28:57
observed that people kind of settle on
28:59
yes this is a zero mode this can be used
29:02
to encode a qubit the questions are now
29:04
of a different nature the questions are
29:05
like can we actually do it in a
29:07
meaningful way so that we can operate on
29:09
it can we actually couple several qubits
29:12
together so that's kind of where we move
29:13
to at this stage and there is a white
29:15
paper out there's on the archive that
29:16
lays out a particular architecture how
29:19
we can scale but kind of on the
29:21
engineering side has several really
29:23
tough problems that that we have to
29:25
solve but I think I think it's pretty
29:27
much settled that we have observed in my
29:29
Arana zero modes yes I should do what
29:31
Allan did and get all the condensed
29:33
matter theorists and experimentalists in
29:34
the room to stand up because there's no
29:36
condensed matter theory yes per
29:38
Mentalist on this panel take it with
29:39
some rocks yes rain of salt any other
29:42
questions you spoke about this idea of
29:49
classical emulators of quantum computers
29:53
so could you tell what is a state of the
29:55
art how many qubits can be emulated
29:57
presently with some computers
30:03
thanks it depends on the computation so
30:09
for yeah I mean it depends on the depth
30:12
it depends on the layout and the actual
30:14
specific thing that you're trying to do
30:17
for the quantum supremacy stuff which
30:21
Alan alluded to earlier which I've
30:25
worked on with Google I know that the
30:27
best classical simulators can
30:30
specifically for those classes of
30:32
problems get to around 45 ish qubits and
30:38
there are real problems and going too
30:41
much further than that because of RAM
30:43
limitations and other things but for
30:47
different classes of problems you can go
30:49
a lot further like you get it you can
30:50
see
30:51
a little more cubits but they're the
30:52
nature paper from IBM about factoring an
30:55
arbitrarily large number with two cubits
30:59
quick advertisement for q-branch
31:02
we have developed an emulator that looks
31:06
at a particular hardware system that has
31:08
been deployed on premises at the
31:10
Commonwealth Bank of Australia the
31:13
purpose for that is to get the emulator
31:15
into the hands of their developers
31:16
inside the bank so that they can start
31:18
to just broaden their awareness and
31:20
understanding of how quantum computers
31:22
work and what the applications might be
31:23
that particular emulator does at least
31:26
30 cubits but again it's very dependent
31:28
on the problem and will continue to push
31:30
that state-of-the-art as we go forward
31:32
okay I've got Jason standing up can I
31:38
can I have one more quick question for
31:48
Matt because you brought up that you
31:49
guys are looking at potential financial
31:51
applications is that just because DE
31:53
Shaw is an investor or I mean what do
31:54
you guys see as you know are the big
31:56
algorithmic capital allocators investing
31:59
in this or yeah the big reason and we
32:02
can say this on a comparative basis
32:06
algorithmic traders principal traders
32:09
have the ability to be really really
32:11
supple in terms of kind of being a
32:15
solution looking for a problem whereas
32:17
in most cases you need to identify a
32:21
problem and find a solution to it so
32:23
again there's this creativity thing that
32:25
that quantitative traders look for to
32:28
try to get an edge and you can look at
32:32
problems both that would be run
32:34
efficiently on Anil errs and circuit
32:36
model machines that could be translated
32:38
into trading strategies and so I do
32:42
actually think that there's an argument
32:44
to be made forget about the underlying
32:46
math and whether you can mathematically
32:49
argue that there's a particular
32:50
advantage to a particular industry for
32:53
quantum computing but but I think that
32:56
the sort of this psychology of
32:58
principled traders and their their sort
33:00
of creativity and and flexibility
33:03
some like an obvious an obvious place to
33:07
explore implementing this technology
33:10
yeah thanks well thanks again and please
33:13
round up up holes for a la palace
33:15
[Applause]
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