Dan: Hello and welcome
to the quantum divide.

This is the podcast that talks
about the literal divide between

classical IT and quantum technology,
and the fact that these two domains

need to become closer together.

Quantum networking actually is
more futuristic than perhaps

the computing element of it.

But we're going to try
and focus on that domain.

But we're bound to experience many
different tangents both in podcast

topics and conversation as we go on.

Enjoy.

Dan (2): Hello and welcome
back to The Quantum Divide.

It's been a while, there's been
a bit of a gap, whilst we've

been collecting ourselves and
preparing for the next few episodes.

This time I've got a
really interesting guest.

We're going to have a bit of a focus
on more of a a business kind of

implementation approach, look at what's
happening with enterprises in across the

globe that are looking at bringing quantum
technologies into their operations,

into the R and D and strategy and so on.

So I'm very happy to be joined here
with uh, Shreyas from Accenture.

He's the global lead of Next Gen Computing
in the technology incubation team.

Welcome Shreyas.

Shreyas: Thank you.

you for inviting me.

This is awesome.

Dan (2): Absolutely.

Yeah.

Thanks.

Thanks very much.

So I'm looking forward
to this conversation.

It's going to be a little bit different
to some of the previous podcasts.

And that's worked well for me to drop
in different types of perspectives.

So this is a new one for us from
the world of consulting and so on.

Let me start with the classic
question that I have to ask at the

beginning of every conversation,
is what's your path into quantum?

Give us a bit of an introduction.

How would you like to, what would you
like to say for us to get to know you?

Shreyas: Wonderful.

Look, quantum is a paradigm shifting
technology, and to treat it otherwise

is to misjudge its potential.

Becoming, for us, or for any
one of the organizations that we

consult, becoming quantum ready
requires a complex combination of

talent, technology, and planning.

We've been at it since the mid
2010s and exploring quantum methods,

applying techniques to practical real
world applications on all sorts of

cutting edge software development
and implementation on today's noisy

intermediate scale quantum NISQ hardware.

Sounds like a mouthful.

And we're providing several
services through this journey.

Everything from experimentation to
strategy, through R& D to talent

and organization and security.

Where shall we start together?

Dan (2): Yeah, so why don't we have
a look at the quantum strategy, first

of all of Accenture at a high level?

Because as you've described there.

Of course, Accenture is
a broad organization.

Of course, you're well known to be the the
organization to help companies build their

strategy, but of course your delivery and
implementation and operations function,

as far as I understand is even bigger.

And that's why the size
of the company is so big.

What's the high level strategy for
quantum and how, how does it fit into

the next gen computing as a whole?

Shreyas: So we have an offering
known as Quantum Foundry.

Quantum Foundry provides an overall
framework of talent, execution

capabilities, technologies, ecosystem
partnership assets and methods to support

the ability to drive quantum initiatives
to scale and drive business value.

Some of the things that we do
in it, it combines the strategy

definition, which agile test and learn
implementations, And it winds up reducing

delivery durations and it outcomes.

And it delivers outcomes,
not just propositions.

It has the ability to quickly ramp
down and ramp up to meet any demands.

So if you were to consider a series
of sine waves, I always start with

this because beautiful quantum
computing, what quantum computing

can't do without sine waves.

We start with the first sine on the top
to, to talk about how do you initialize.

Any sort of experimentation, researching,
we explore through the various different

use cases that's relevant for these for
a business, for an organization with

partners at the center of how do we
get the best technology suited for you.

And finally, through whatever
understandings that we've had to research

and understanding through what the
technology is capable of it, we got

started getting to developing these real
use cases that delivers business value.

with the hopes that we might one day use
that technology in a scaled proposition.

Now, all of this won't be possible
without actually knowing what it is that

you're driving towards, what the North
Star is, and our strategy teams provide

both a tactical as well as a future
forward looking perspective into what

are those problems, how can we solve
it, how do I get my organization ready?

What is the appropriate sourcing
that is right and crucial with all

the capabilities that is provided
by any talent oriented technology

that's out there to enable the
right skills for the right roles?

That means the partnering through
academia or partnerships through

vendors or through recruiting.

There's technologies like Pluralsight
that uses AI to find the right skills

within the scarce market that we're in.

That's just to give you a
brief perspective into what

is the overarching strategy.

And quantum is huge.

So you apply this through computing,
sensing, communications, security.

Dan (2): Okay, thank you.

Was there a platform in there?

It sounds like you described that
there was some kind of platform.

Or is it more of a service
orientated approach?

Shreyas: You're right.

So it's very service.

And there is a platform, there
is a technology to enable

whatever business proposition
that may exist for our clients.

The platforms could vary.

It could be leveraging a
strange works platform.

It could be on the IBM Q system It could
be on the quantaniums hardware Through

its platform and the other platforms we
are very well and very comfortable with

whether it's azure quantum or aws bracket

Dan (2): Okay.

Okay, understood.

Thank you.

Listen, I'm intrigued.

I don't get to talk to very...

very many Accenture consultants
which is ironic having worked

with you guys for a while.

But I'm intrigued to
know about the process.

Do you have a structure that you follow
when it comes to advisory and then

identifying what's relevant for the
customer, helping them execute it?

How do you measure that going forward?

You must have some kind of grand
scheme that everybody follows

internally, but I'm sure they,
everybody takes their own path, right?

But give us a snapshot of that.

Shreyas: You're right.

And, there's a methodology.

Every madness, every chaos has a certain
amount of methodology within what we do.

I'll start by saying, our vision for
this journey is to be vendor agnostic.

to be business problem focused
and leveraging its ecosystem.

So you want to identify the
appropriate business problem.

Enterprise use cases exist
across nearly every vertical.

Both vendors and Accenture quantum
teams are ready to deliver these

very specific use cases across
this industry that delivers value.

To give you an example, when in the
case of financial services, you talk

about what is currency arbitrage.

We, as we go by that as just an
example and start identifying what

are the potential key business
benefits or relevance in the

marketplace for that client?

And we had tried to assess what
is the potential importance of it?

Money is at the center of everything.

So what is the potential disruption
impact, potential revenue impact in the

future and when it could be realized.

With that, we try to understand
very specifically what are some

of those classifications, right?

So with this business problem, you
have to then assess it to what is

the problem classification or the
computational problems that you

can essentially use to solve it.

Think about it as the best way to
align it is if you have mathematical

primitives that you can align it with.

Whether it's these mathematical
primitives that you have to create

from fresh or custom created.

You got to be architecturally sound,
algorithmically unique, and it has

to be quantum runnable in some way,
shape or form, what's being modeled.

And so we talk about it in terms of
how you go about modeling it, what are

the complexity factors, what are the
different algorithmic implementation

considerations, and what are those
quantum computational approaches.

And finally, with that, we want to
start with the best in class quantum

hardware, whether you're selecting a
classically quantum inspired or physics

inspired, or finding a way to do a hybrid
quantum in this NISQ era, leveraging

the best of tools across the board.

So that's, in essence, how we go about
and our vision and our methodology for

these quantum computing based problems.

Business problem.

Problem classification, hardware, you
find the appropriate mapping with all

of that, and it would work for you.

Dan (2): Cool.

Yeah, it's there's a lot of
different tools available

to you on the market, right?

This is the thing.

I expect it's a highly
tailored solution every time.

Listen, when it comes to, where we
are, In terms of, the technology.

Okay, we know there's lots
of different modalities.

No doubt yeah, you've
mentioned multiple platforms.

There's also different ways
to program these systems.

But ultimately, the end result is
to be able to implement something

that provides some advantage
over classical computing, right?

The...

The phrase quantum supremacy gets thrown
about, I think that's a bit too strong.

I think quantum advantage,
where can you save energy, time,

money in solving a problem?

Or how can you solve a problem
actually that isn't possible

on a classical computer?

What's your opinion of where
we are in terms of, human

race's trajectory towards that?

Shreyas: I love this question.

This is one of my favorite
questions, because benefits starts

from asking yourself the question,
and you ask yourself, you start

the journey with, what is it?

And we all know that recent developments
have propelled quantum computing

into tangible computing options.

So once you ask what it is, and
you asked me this question in

2016 or 2017 when we first started
this Implementing it for clients.

It was education.

We start off by educating ourselves
on what this is Platforms such as

what you're doing is just paramount
for those beginners So then you go

by asking the next five questions.

How can it be used?

What value could it have?

Can it scale?

Can we really transform
industries and market?

And Can we create or can we
create new products in the market?

Imagine this in a continuum.

That continuum spreads over decades
since we've initially started, right?

If you'd asked me this thing
in 2020, how can it be used?

I did this using proof of concepts
or proof of value, which gets

me to what value could it have?

This is the methodology
that you want to apply.

This is applied and realized.

Back then and it is applied and
realized today where you go through

these proof of concepts and value to
try to understand What can be solved?

Can it scale?

This too is Researched through POCs
and various pilots Imagine solving a

business problem that has a workflow and
this workflow is condensed into a lot

of very NP hard Problems that you want
to say you can solve these things today

the current size of the computers and
it's The NISQ era prevents it from being

full blown and scaled into production.

However, you can work through,
architecturally, a solution that

could be quantum inspired through,
through digital annealers, for example.

And there are a couple of examples that
are out there that we can find out.

The question of whether it
can be transformed industries

and markets it's arguable.

That would require you to be in
production, scaled, and fully researched

with a perfect system in place in this.

Advantage.

You've technically started to
transform industries by asking,

I'm solving these NP hard problems.

You're on a good path.

Can we make it into new
products in the market?

It's the same thing.

You got to have a scaled device and
scale solution in production that

is managing billions of dollars.

Not sure that we're there yet, but I think
by asking these questions you are creating

the niche market where new products can
be entered into the market, potentially.

To your point, there are several tools
out there that can see this happen.

To be agnostic, At this stage,
it's important and, companies like

Strangeworks allow you to tap into
all of these different devices.

Whether it's classical, or
if it's, quantum inspired, or

in, in, it's this NISQA era.

Dan (2): Yeah, I think there's a
whole different set of layers of

whether you call them orchestrators
or hubs or something that allow you

to use multiple different modality
computers that are popping up.

It's it just goes to show how quick
things develop at that level, when in

fact at the back end, when it comes
to the computation, there's still many

hurdles that we have to jump over.

I just picked up on a couple of words, you
said that continuum I'm aware of that's a

favorite word of the Accenture, isn't it?

There, there's the cloud continuum, right?

Which was a big go to market.

Then there's the, I've seen that
there's a metaverse continuum.

Why isn't there a quantum continuum?

Maybe, maybe me calling this
out will make you think, Oh

yeah, I should be doing that.

In which case, I'd like you to
give me all the credit for it.

When you release the documents.

Shreyas: Thank you for helping me change
and create this, another new business.

I'm using quantinium in a not as a
noun, but more of a way to describe

where it falls into the overarching.

Dan (2): Yeah, totally get it.

Shreyas: It's there it's coming, and
I think the business is growing and

we'd very much love to be a part of
everyone else's business to help them

see that same thing in this continuum.

Dan (2): Tell me if, if we're still in
the world of possible and probable, rather

than proven out use cases, why are your
clients investing now if there isn't a

clear map ahead to that quantum advantage?

I know, of course, every solution is
different, every customer is different,

and I think we'll come onto a few
use cases in a bit, generally, why

do you think customers are investing?

Shreyas: It's important to
identify the very specific use

case you want to have, right?

And I'm going to get into a
typical consulting mode over here.

Imagine yourself.

In a typical chart like this, right?

So in here you have multiple quadrants
and the quadrants is associated in

the Y axis by what is the business
value and on the X axis is what is

the feasibility, where can it be done?

There are technologies that have high
business value today and it's that same

business value that I articulated that
has the disruption potential or the

potential revenue in the coming years.

And you would have heard a
lot of these things, right?

So when we talk about what is feasible
today that hasn't that is feasible

in terms of a research, right?

So that's feasibility.

Your feasibility is you have research,
you've got point proof of values, you've

got a pilot, and then you've got scaled.

That is feasibility.

Research element, you can already see
business value and working on it, right?

So you talk about these
generative applications.

You hear Gen AI come in every
single way, shape or form.

Tapping into what you can do to make these
devices better have incredible value.

In applying today's classical
computations and techniques

to advance quantum computing.

You can also have very specific
use cases and there are very good

examples of leveraging, quantum
random number generator for

classical Monte Carlo use cases.

There are examples out there where
you can deploy that in production.

I should be careful to say that it's
not truly production, but it can be

deployed in a scaled permissible manner.

You then have.

Use cases that are highly scale,
like you, you hear using technologies

digital annealers today in solving
these very hard problems at the

scale that we all think about.

We started experimenting with
that early on with BBVA and we

did three different use cases.

It was around trading trajectories
portfolio optimization

and currency arbitrage.

Back then it was a huge challenge of
what is latency, how can I solve for

that, how can I reach that huge problem
statement that I want to solve for.

Technology has improved,
as I said initially.

It's ever changing and the
potential exists today.

Where you can't ignore it, and
you're planning for it, and

you're actually deploying a lot
of these solutions as pilots.

We have a couple of those examples of
what could be a relevant pilot, like

with the telecommunications industry
doing optimization problem, or in

the case of a digital annealer doing
a similar optimization problem for a

bank, or, and the list goes on, right?

I hope that answers the question.

Dan (2): Yeah, listen, I love the fact
you went straight to a two by two matrix.

That's perfect.

Stereotypical consultant.

No, I love those things and you can use
them for so many different things, right?

I'm familiar with the product market
diversification one, the leaders of

visionaries the whole performance
versus relevance, which I think is the

one you were talking about where we're
really quantum, where does that sit?

And yeah, I think the readiness point
of it, is It's going to influence how

important it becomes for people in time.

So yeah, thanks for mentioning the BBVA
example Are there any other customer

references you'd like to talk about?

I'd be interested to hear even if you
can't mention the names just a view

on the use case the the industry,
the outcomes, those kinds of things.

Shreyas: Sure.

It's a, I'll start with some of the
more recent ones and work my way

towards what happened in the past.

So you get an idea of
how times have changed.

We developed a business experiment
use case with a large French

insurer to compute their insurance
portfolio based on some metrics.

And one of the things that we achieved.

in this quantum advantage era is
by formulating the problem with a

quantum philosophy and executing it
on real quantum inspired hardware.

Reinsurance is a practice whereby
insurers transfer portions of their

risk portfolios to other parties by some
form of agreement, thereby reducing the

likelihood of paying large obligations
resulting from an insurance claim or

multiple different insurance claim.

And so one of the things that we did
is we had data for simulated real

world events of these losses the
cost to these insurance portfolios.

And so we wound up really optimizing
one which was not done before.

By surely doing that we were able to
do something and prove that could be

run on a quantum computing device or a
quantum inspired hardware and that was

truly relevant and truly one of a kind a
Somewhat just before that is by doing a

record breaking million core simulation
of PFAS, of the PFAS formulation.

is otherwise known as the
forever chemical, right?

Per and polyfluoric acid substance, right?

Now, PFAS are artificial chemicals
that are used in packaging, paint, et

cetera, and they do not biodegrade.

And they cause really detrimental
health issues like cancer.

Computational chemistry can speed up the
R& D process for understanding how do I

wind up breaking up this complex model.

And one of the things that we did is
to accurately simulate this intractable

problem and run it on on a very high
powered HPC that we had to combine

in AWS to solve for this IFCI type
of model in a custom HPC in AWS.

And one of the things that we did
is we did this because of a two

year collaboration that we have
at I CHEC, Irish Center for High

End Computing in PFAS chemistry.

And we started doing this thing on a
quantum computing device and, understood

what could happen and eventually worked
our way through what could be a massively

scale up using the good chemistry
software to run this millions of vCPU

cores in non reserved resources of AWS.

And the impact was just accurate energies
for breaking this carbon fluorine bond

in the three PFAS molecules resulting
in really good scale and accuracy.

And so this is something that's
going to help the scientists study

the PFAS destruction methods more.

And it's essentially at the point
where it's paving the way for on

demand cloud HPC as an affordable.

Scalable paradigm for
scientific computing.

So I'm giving you two examples, one
in the area of finance, one in the

area of chemistry, both to a certain
degree simulations and optimization.

If I were to touch on machine
learning, the most common one that

we talked about in machine learning
is around fraud detection, right?

It's a banking use case.

It's very important for the sector today
because it shows whether a transaction is

counterfeit And so QML algorithms shows
how to detect a fraud banking transaction

based on a certain set of variables.

And so we, we've tried it using
multiple different methods, including

a quantum support vector machine,
which was used to foresee what

are these true false transactions.

It's like we said, it's very
business minded, so it's performance

of the solution versus classical
algorithms were interesting findings.

Where we were able to get higher
training accuracy better inference

confidence scores associated with
it, and a definite reduction in false

positives off of the data sets, and
we did this using a data set that was

available through the Spanish banking
on one of the Spanish banks that we did.

So I touched on simulations, mass
optimization, and machine learning, and

I, it's each of them, two of them with
a bang, one from a chemistry standpoint.

Please let me know if I need
to touch on anything else.

I'm happy to provide some insights.

Dan (2): Yeah, that's fascinating.

Thank you.

I want to start with the chemical one.

But just truly because this is
well, it's totally foreign to me.

I love the fact that you went from HPC
and then to a quantum device of some kind.

Shreyas: Oh, we started
with quantum first.

I'm sorry.

I should have said that.

We started quantum HPC.

Dan (2): So what drove that decision?

Was it that you needed more
computations that couldn't be

implemented on a quantum computer, or?

Shreyas: See, it's
various different things.

And I think they both hold promise today.

And I will, we would say that it allowed
us to learn exactly how to model the

problem and reduce the appropriate problem
size to solve it in a real world quantum

computing device and successfully solve it
for a certain Subset of the PFAS molecule.

So it provided us insights and intel
into what of those things could I run

and up to how much for instance, we
could clearly simulate, H2 molecule

with 4 qubits, lithium hydrate with 12,
H2O with 14, going all the way up to 46

qubits with carbon, hydrogen and oxygen
keep forgetting what that molecular

structure is, but it was a rather complex
molecular structure where we applied.

different techniques to solve for
that very hard problem, and I think

I believe it was a methyl fluoride
and Trifluoromethane, I think

Dan (2): I'll take your word for it.

I'll just nod and say yes.

No, listen, so I suppose what you're
not doing here is implementing, you're

not computing on the qubits per se.

You're using the Hamiltonian of
the system to try and simulate a a

compound or the behavior of a compound.

Is that right?

Shreyas: You are trying to find out what
is the appropriate energy that it's as

soon as I hit these specific energies
For how much I need that energy to

break That molecular substance further

Dan (2): So it is a calculation.

It's a series of
calculations which are made.

Okay.

And yeah, what are some of the
complexities in there, right?

You've got all the different positions.

I know you're not a chemist,
but maybe you can help, right?

You've got all the different components of
the molecule which carry their own energy

and bonds, which have their own energy.

Is it a simple as a.

A lowest bounds or a kind of
optimization problem to find out

where the weakest point is, or I'm
sure it's more complicated than that.

Shreyas: I, I wish it was
definitely more than that.

And I am by no means a chemist but
would love to have the opportunity

to bring up a chemist who worked
on this one to speak more about it.

Dan (2): Okay.

No worries.

Okay.

Yeah.

Thanks for the use cases.

It was it's interesting to see
two from the finance world.

This is a kind of the second time we've
touched on quantum for finance in my

podcast and it looks like it's I think
the ROI, the monetization factors are

going to be stronger in that domain.

In some senses, I think so.

And it's also very competitive, right?

If you think back to

Shreyas: It is, it

Dan (2): When the the exchanges first
started getting built and the, connecting

them up and trying to get lower latency.

I see this as a kind
of an analogy to that.

If you can perform a financial calculation
a lot quicker than your competition,

there's a chance you can capitalize
from that somehow, whether arbitrage

or just getting into, particular
action before the competition, right?

Shreyas: Yeah, no, absolutely.

And by the way, in 2019, JPMorgan
Chase, Marco Pistario and his team

are doing a marvelous job with this.

In, 2019, 2017, and there were a lot of
very basic examples on what could be run.

And it went slowly to demonstrating
a prototype for the quantum

algorithm in a meaningful way.

Then getting into introduction of
abstraction layers to allow for better

performance and ease of experimentation.

And now we are Slowly pushing the
boundaries of what a quantum computing

machine can do today with the hopes that
in the future You're going to apply each

one of these different things that I've
learned on Actually making it like do

those billion dollar decisions, right?

So in derivative pricing just to give
you an example you start by understanding

what is the Black Scholes and quantum
Monte Carlo formulations that are out

there and between 20, 2020 2023 you
hear the different styles of options

and whether it's European style
options or, path dependent options.

Then in the future, the hopefulness
is, I have path dependent options,

exotic options, with additional
Greeks and risk and hedging.

You tap additional currency arbitrage
or fixed income derivatives.

Getting into American and decision
based options and so forth, all in

the derivatives and options portion.

QML and classification is the same thing.

We did feature selection with a
group of theoretical properties.

The hopefulness is now I'm going
to add larger data sets, add

things like anti money laundering,
product recommender, forecasting

financial crises for this QML space.

And, optimization with convex
constraints with the hopeful next

that you reach more realistic with
complex non convex constraints.

I can go on, right?

And chemistry and the pharmaceutical
business is going through a very

similar turn as well where you start
by searching through a large search

space on what is molecular similarity,
which is what we did with Biogen.

to, today, trying to model complex
molecules to understand them better,

to make drugs more effective in
some instances, or make it faster.

Dan (2): Fascinating, Shreyas.

Thanks.

Yeah.

Hey, earlier on you mentioned quantum
communications, quantum sensing and so on.

Let's dig into a couple of those.

And start with sensing.

I think this is another field, which
yeah, it seems more and more I dig into,

I realize that there's products either
on the market or, due to investments,

through government, governments and
so on, there's technologies out there

for timing and sensing, which are

Coming.

So what engagements have you had
in the world of quantum sensing?

Is this a a field for you or not?

Shreyas: We wrote a point of view
on this one, and we published it on

a quantum communication standpoint,
and I think there are a lot of

use cases that are out there.

If you think about it in terms of,
positioning, navigation, and timing,

or medical imaging radar, lidar, and,
some other astrophysics and astronomy

based sensing, metrologies, I can go on.

So there are a lot of
use cases in those areas.

And you want to be mindful of for
each one of the different areas in in

quantum sensing that you have to be
mindful of timing, rotation temperature

acceleration, magnetic field, imaging,
detection, and electric fields that

is code that and their specific techs
across each one of those things that

shows how they're relevant for these
specific use cases and where its

relevance and when that relevance is.

Like you said, many of these
things are today, right?

If you, if gravimeter.

That's, I can see a lot of those use
cases happen today and it's ready today.

When you're talking about CSAS
in terms of timing for all these

different use cases, you can see many
of these things applicable today.

But if you go down to the magnetic
field for atoms across these

different things, they're 10 years
away or even detection on any.

degree whether it's superconducting
nanowires, for example, it's not

there today from that standpoint,
at least not that I'm aware of.

And it's, I don't know,
maybe six to 10 years away.

There, there are, a few use cases
that's, there today and, we won't

discuss any ongoing work in this space,
but, we can definitely say that we're

interested in helping businesses build
and manage their quantum networks.

Dan (2): You mentioned
detection using nanowires.

Could you elaborate on that a little bit?

Is that a particular
detection of what exactly?

Shreyas: So when we talk about detection,
so in those specific scenarios you have

geological imaging or gravitational and
magnetic field imaging associated to it.

So like in the in, in terms of say
oil and gas and mineral exploration.

or archeology or seafloor mapping
or in, in the case of I think I said

around metrology as well, whether it's
magnetic field calibration or quantum

illumination, you're going to have to
create these superconducting nanowires and

the technologies associated to it to make
the detection more active, so to speak.

Not to say there are other sensing
portions of it around acceleration that

can't do that today, but specifically
around superconducting nanowires that

to do these sensing detection, it's
it's not something that I've witnessed.

Dan (2): Yeah, that's definitely
one for me to go and read up on.

So what about quantum networking?

This is the main focus of our...

of this podcast series.

I'm sure cryptography
comes into your response.

But if you could give us a broader
view on other things like connectivity

between quantum computers and any kind
of conversations you're having with.

with your customers on that front.

Yeah, give us a summary of what's
happening when it comes to that domain.

That must be more active for you at
Accenture, in terms of the questions

your clients are asking and the
support they need in preparing

their networks and their systems.

Shreyas: Yeah, absolutely.

And, you hear this thing all the
time around harvest now, decrypt

later, and that's used as a
mechanism in almost everything.

The one thing that we are going to say
is that we're definitely interested

in helping businesses build and
manage their quantum quantum networks.

And anyone looking to create quantum
networks for security or research,

Needs the management capability on the
top of the physical hardware and, for

example, just as an example, that's
where Aliro comes in the software,

Aliro's controls plain software comes in.

It has a network simulator that can take
in these existing fiber infrastructures

deployment details and estimate how
efficient communications will be

over currently any available of those
channels that's out there for them.

And this enables planners to
understand where to add infrastructure

and to gain the most benefits.

Dan (2): Okay.

So let's talk a bit more about
The quantum communications and

networking piece, if that's all right.

I just want to continue to
talk about cryptography a bit.

So yeah what's the general trend
in, in Accenture among your clients

when it comes to cryptography?

Is it mostly that, obviously there are,
there'll be clients who are proactively

making changes on their infrastructure,
there'll be clients who are perhaps

making a slightly more conservative
strategy and kind of preparing rather than

proactively pushing out Kyber and so on.

... Can you give me an idea on the trend?

Shreyas: See, look it starts
off why, we, we do things that's

meaningful for our customers.

And we know quantum is coming.

94 percent of these enterprises
cite that quantum computing as a key

security concern should be addressed.

Now, off of our research that we did,
about 50 percent of them, 51 percent

of them describe the risk function as.

At best only is somewhat effective
and responding to these rapid changes

and, we, we realize that these
vulnerabilities, risk concerns, and

datas are exposed and threats exist.

And we fully understand that industries
or companies needs to meet also the

skill gap that exists inside of it.

Wind up understanding what those
legacy tools are and securing those

and figuring out what the cryptographic
gaps are and A lot of times that

becomes a manual process, right?

So if you were to think about this in
terms like if you asked me before 2022

It was all about readiness and then
we all know post 2022 with standards

that are coming about soon That NIST
is creating, NIST report 8240 was out

there to talk about what should be done
We are looking at it in terms of what

are those crypto agility accelerators
where a the risk graph is basically

scored based on a set of risk templates
and alerts that are generated if the

scores go above a certain threshold.

Crypto agility accelerators.

are something that we are pushing
forward to understand and quickly

update what is the cryptography across
enterprises to meet these demands.

And we think about it as why now, right?

There are improvements to machine
learning and crypto analysis

that are being exposed today.

We know there are, advanced
persistent threats to APTs.

You may encounter.

And APTs essentially are collecting these
encrypted data today with the hopefulness

that they will decrypt it in the future.

And of course, crypto agile
businesses just need to quickly

respond to these changes.

Like I said, those 94 percent of
those enterprises cite quantum

computing is a key security concern.

Only 51 percent of them say
that the risk function actually

is ready to respond to it.

And those are some very
daunting statistics.

And I think, bearing that in mind and
being quick to to react and to plan for

this, that timeline is today, right?

Where engineering of systems for
practical quantum computers are coming

about when it gets to 2025, 2027 and
beyond, perhaps when these production

systems are solving these problems.

You'd want to think about how do I
quickly prioritize and find out what

those remediation activities are
to implement remediation activities

and track them going forward.

Dan (2): Yeah great point to bring it
up to a risk conversation, because I

guess organizations have many different
risks they have to tackle with.

And, the risk management function
there is to, the function is to grade

and kind of prioritize and so on.

I guess it's quite difficult for
risk managers to really know, when

the risk could actually happen in
terms of the current crypto standards

that are used in an organization's
systems being vulnerable.

So it's quite difficult.

It's like a, it's a known unknown,
and that makes it difficult for a risk

manager to try and stack it up against
operational risks, for example, which

they have in their environment day to day.

Shreyas: I I 100 percent agree to that.

And that's where a lot of what
we've built so far, those sort

of offerings, like whether it's.

What is your quantum readiness?

Which, we talked about before, but
in terms of security is one aspect.

Crypto agility, where you want to
design and implement and maintain these

crypto systems for quick adjustments, if
needed, it's also important and to pull

together, I'm going to go back to the
matrix, the two by two matrix, right?

Where you have low to high of
your security threat, short

term, mid term and long term.

And to be able to articulate that Again,
over the continuum it's important and

to identify which of those things have a
change in operational cost, which of those

things have changed in security level.

So you can cover, everything in terms
of confidentiality, integrity, and

authenticity to understand it more.

And by the way, there are
crypto agility toolkits and

SDKs that are out there today.

Which we essentially take in, we take
forward and try to apply and, we, we

built ourselves multiple different assets
around readiness and transformation,

crypto agility scripts and so on
and the foundations around it to.

To make it simpler to understand and bring
it up a level to these risk managers,

so to speak from a security standpoint.

Dan (2): You mentioned
Crypto Agility Accelerator.

Is that a service that you guys
offer to include assessment and

preparedness and things like that?

Or is it more of a generic
term used in industry?

Shreyas: It's part of a series
of offerings that we have.

So we use accelerators where we have past
research, past work that we have done.

Lest it be with remediation scripts or
quantum services that are associated

with it, or reference architectures
the security capability journeys and

additional risk assessment components
and scripts associated to that.

All of those things are accelerators
that we bring in to a specific delivery.

To do our job, basically make it easier.

There are certain examples of how
we have platform deployment, whether

it's an Azure or AWS instance
scanning and assessment tools for

recommendations or demonstrations and
scripts that we bring in to make it

a lot easier to do the the testing,

Dan (2): A lot of this comes down to
putting the risk piece to one side for

a minute when it comes to the actual
exercise of assessment and Implementation

or decision making in between, then
it's really just about data collection.

Isn't it?

It's trying to get all of the the state
of play of all of the crypto across the

organization to then try and work out
what could be crypto agile and what can

or cannot support next level algorithms.

Shreyas: And there are tools
in here as well, right?

Like PQC libraries and available
in AWS and Azure, the system post

quantum cryptographic world where,
you know, you, you bring in some very

critical data, like you just said.

As well as cryptographic methods
that could be used today to implement

these post quantum cryptography for
selected areas, and there, there are

tools and softwares that are out there
today that will help make that better.

Dan (2): So let me ask
you what's next, right?

So you've described in fantastic
detail, some of your experiences,

some of your engagements, the
strategy of your organization

what's next for you considering
what's coming in the quantum market.

Are you guys focused on any
particular area in any way?

Of course you'll be supporting
customers in all industries any

way that they need help, but have
you got any particular focus maybe

in the next 6, 12, 24 months or

.
Shreyas: I think in the
next six months, right?

So we...

We want to start to understand,
work with our clients, first of

all, understand their problems and
actually find ways to solve these

things across every single industry.

I think every industry is impacted
today, as I said, and the potential

disruption is seismic in nature.

Like it's huge, seismically
disruptive today.

And so almost everybody
is going to be impacted.

And so my focus is to, personally,
to grow and understand how our, how

companies will make themselves both
secure and function in their business

standpoint better through these next
generation computing technologies.

Next gen computing technologies
across let's it be quantum

computing or neuromorphic or
general purpose matrix processing.

Huge word.

General purpose matrix processing is
basically your HPCs that we talk about.

Dan (2): neuromorphic as well?

That's

Shreyas: Yeah

Dan (2): I'll see if I can get that
into my my, my bingo game tomorrow.

Shreyas: These are just some of the
things that we just want to focus in

on to just bring the right value to
all of our clients and so if they see

value in doing this exercise through
us, we would be happy to help with them.

Dan (2): Fantastic.

Okay.

Listen, I think I'm going to wrap there.

So it's been a fantastic conversation.

I've learned a lot as per usual.

Thanks for bringing all that knowledge
Shreyas and take care for now.

Dan: I'd like to take this moment to
thank you for listening to the podcast.

Quantum networking is such a broad domain
especially considering the breadth of

quantum physics and quantum computing all
as an undercurrent easily to get sucked

into So much is still in the research
realm which can make it really tough for

a curious IT guy to know where to start.

So hit subscribe or follow me on your
podcast platform and I'll do my best

to bring you more prevalent topics
in the world of quantum networking.

Spread the word.

It would really help us out.