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Entangled Health with Thomas Ehmer is a long-form podcast and research-media project exploring quantum biology, quantum computing, AI, biotechnology, consciousness research, and the future of medicine.
I've spent years at the intersection of digital health, AI, consciousness research, and the quantum frontier - computing, biology, sensing. But the conversations that actually needed to happen weren't happening. So I built them.
The approach is deliberate: every episode, I go in prepared, and ready to throw all of that out. One question I've been obsessing over all week. The goal is to care about it enough to go deep, and not so much that I stop listening.
This is not a science podcast. The best conversations go wherever the thinking actually leads, into philosophy, into personal history, into what it means to live at the edge of what's officially permitted to be true. The best moments are usually unplanned.
My guests are hard to describe - frontier scientists, engineers, quantum biologists, AI researchers, clinicians, composers and athletes whose knowledge doesn't stop at the edge of one discipline. I know most of them. They don't always know each other. And you probably don't know them yet, but you should. They're working on your future. Entangled Health is where all of that finally meets. Sometimes the most important thing an episode produces isn't the recording, it's the introduction.
Every episode is a Deep Dive. Some just go deeper than others.
These are slow conversations, 90 minutes or more when the thinking demands it. They end right where most shows would begin. There are no formats, no segments, no rapid-fire rounds. Just two people staying with a question long enough for something true to emerge.
No other podcast uses quantum randomness as a design principle. Once per episode, a real quantum algorithm running on IBM's quantum cloud selects one question from the listener community. Not random like a hat draw. Quantum random. Because the universe's uncertainty isn't something to optimize away, it's where the good stuff lives.
Join the discussion & find transcripts: https://thomasehmer1.substack.com/
Watch on YouTube: [https://www.youtube.com/@EntangledHealth]
Host: Thomas Ehmer [https://www.linkedin.com/in/tehmer]
Humans don't reveal what they actually think at the speed of a swipe. They reveal it slowly, when the trust is there, when there's no rush, when the question is real. This show is built on that principle. We don't simplify for comfort. We entangle. Subscribe. Entangle. And if you like it —> thanks for leaving a thumbs up wherever you listen 👍
(Note: Entangled Health with Thomas Ehmer is an independent project. It is not affiliated with The Entangled Health Podcast hosted by Madison Murphy Barney.)
But the question that we need to
ask now, do we really solve the
basic problem of healthcare and
life science?
Not really because we just
scratched the surface, right,
Because we still need to
understand what is anti
microbial resistance, right?
Or what is, what is a cancer,
what is gene therapy, you know,
stem cell research.
These are all unsolved questions
for us, right?
And my strong belief is that we
need a tool such as quantum
computing to really go deep into
the understanding of the
biologic chemistry and physics.
Welcome to Entangled Health,
where we explore the
intersections of quantum
science, AI, biotechnology, and
human well-being.
My name is Thomas, and I'm
convinced that health innovation
happens at the entangled
boundaries where cutting edge
physics meets biology, where
consciousness research
intersects with technology, and
where visionary thinking
transforms into practical
solutions.
We are bringing together
pioneers, researchers, and
innovators who are reshaping how
we understand and experience
health.
My guest today is Doctor Gopal
Khalmor, Global Quantum Lead for
Healthcare and Life Sciences at
IBM.
We talk about his journey from
computer science research and
pioneering machine learning in
pharma at Noble Nordisk to
driving quantum innovation at
IBM.
Gopal shares how IB M's quantum
strategy is shaping the future
of drug discovery and digital
health, where the real impact
for patients may come first, and
what it takes to bridge machine
learning, clinical reality, and
cutting edge technology.
We discussed the most promising
near term applications of
quantum AI in pharma, how
startups and large enterprises
can collaborate on quantum
health solutions and we will
learn how personal values Dr.
innovation in such a fast
evolving field and much more.
A quick disclaimer, all checks
at Entangled Health are a Co
creation between the guest and
myself and not an official
statement from our industries.
We are here in a personal
capacity, so everything we share
is just our own take, not the
official view of any company nor
of my employer or any other
organization.
To add a little bit of dopamine,
you can submit questions to the
Antango Towers questions pool
and later in the show we'll dive
in and the quantum algorithm
running on real hardware picks
which question we answer.
Our guests have their question
to the pool and then answer it
live.
And if we reach 1,000,000
listeners, we might change the
rules, but that's a bit far out.
Stay tuned.
And who knows what the quantum
buys will reveal.
Yeah, welcome back everybody to
the quantum computing flavoured
sessions.
Welcome, Gopal.
Thank you Thomas, have always
pleasure to talk to you.
So can can you just share what
came to your mind when I reached
out and what your definition or
interpretation of Entitled
Health is?
Now, first of all, thank you
very much for inviting and when
you got an I got an invitation
for this podcast.
This really resonates the title
that uses entangled health,
right?
Because for me, entanglement
that's basically coming from the
quantum mechanics and so by the
quantum computing.
But really it's about the
correlation between these two
words, right?
Healthcare and entanglement,
right?
So if you really see healthcare,
Healthcare is nothing but like
understanding the biology, you
know, and the biology is driven
by the principle of chemistry,
right?
And we know that the chemistry
is governed by the principle of
physics.
And we know if we really want to
understand the mechanistic
understanding of medicinal
chemistry, we have to really go
into the deep into the subatomic
level, right?
That means the superposition,
entanglement, interference on
the basis of which the quantum
computing is best.
So for me I think this is really
nice to really have a
correlation between if you
really want to understand the
healthcare to the deepest to the
corest level, I think it the
quantum computing is the answer
at least one of the two possible
2.
It's not scripted.
Thanks, Gopal, And.
Maybe if I can just elaborate on
more on this one, right?
Because I mean, I had a thought
really about this was like, what
exactly should be the intention
for this podcast, right?
I really want the listener
should just get a understanding
about this, not the over hyped,
right?
Or hyped or there's a lot of
things are going on this one.
But I really want that whenever
you talk about the healthcare
and life science, it's something
that the problem that our
ancestor has been solving from
from decades from from ages,
right?
And regardless, from where we
come from, be it a traditional,
you know, Indian medicine, you
know, African medicine, American
medicine medicine, there has
always been a challenges because
all these methods where they did
not have a blind test, right,
like a placebo-controlled what
we normally have, right?
Yeah, yeah, yeah.
It was all based on evidence,
right?
And there was no primary, The
primary focus was only on
treating symptoms and not the
cure, right?
So, and then it took us a lot of
time to understand what is the
targeted therapy could look like
until the, the, the German
scientist Paul Ellis came in
1936, basically gave the concept
of targeted therapies, right?
And this was the basically I
would call the beginning of
modern discovery where we are
applying tools to make
mechanicalistic understand what
diseases, how to treat diseases,
right?
And of course, there has been a
lot of development after that,
you know, you know, the human
genome project, you know,
designing drugs on a computer.
And recently Fast forward, we
can just talk about large
language model AI, you know,
operating structures of proteins
using Rosetta fold, alpha fold,
whatever the name you take.
But the question that we need to
ask now, do we really solve the
basic problem of healthcare and
life science?
Not really because we've just
scratched the surface, right?
Because we still need to
understand what is anti
microbial resistance, right?
Or what is what is a cancer?
What is gene therapy, you know,
stem cell research.
These are all unsolved questions
for us, right?
And my strong belief is that we
need a tool such as quantum
computing to really go deep into
the understanding of the
biologic chemistry and physics.
So that's what I'll call the
quantum.
The Entangled Health is a act
kind of a title for your
podcast.
I'm very happy to continue.
This is cool.
Yeah.
And, and I mean, we could expand
it and This is why I have other
guests also outside from, from
different domains also that the
body is not only biochemistry,
but also soul, you know, and
spirit and mental health,
physical health, sports,
nutrition, etcetera.
But I'm I'm happy to really go
deep also on, on let's say your
expertise, which is computer
science.
I got to know Gopal back then
when he was not at IBM, but
rather at another company on on
our side of healthcare.
And then he switched over to
what I sometimes called dark
side.
But it's just jokingly.
So Gopal, can you just walk us
through and our our guests
earlier your journey?
You can start at school,
kindergarten whenever you want.
Happy to you.
Happy to you.
I mean, so I was born in a small
town called Tumsar which is like
away from 100 kilometers away
from city like Nagpur.
But I know that international
crowd has no idea about these
two.
So just to give a reference is a
400 kilometres away from Mumbai,
which is a part of of course,
yeah, the financial capital of
India.
So actually I was very average
student while growing up, you
know, but I have always
fascinated with physics and
biology from the beginning,
right.
The Fast forward, I did my
bachelor's in electronics
engineering.
So when people talk about
Josephson Junction, I get
excited.
So, and of course, I did my
master's in medical software
from Manipal University.
Then I got a scholarship.
The first scholarship that
basically gave me an opportunity
to go outside India and explore
what is happening outside was
the Fron Hoffer Institute in
Germany.
OK.
And then I had a chance of
working with a really smart
scientist working on a chemical
structure reconstruction
project, which wasn't from Fron
Hoffer Institute.
So it was really nice.
And then got a scholarship from
there in Denmark doing my PSD in
science.
It was an industrial PhD with in
collaboration with the
University of Copenhagen and
Nordic Bioscience.
It's a Danish pharma company
where my title of my thesis was
develop an imaging biomarker for
best Cancer, right.
And then just to tell you about
the challenges, right?
I mean, because normally those
days it was very people,
everybody was developing CAD,
you know, computer aided drug
discovery.
OK, I, I see tumor, I can find a
delineation of tumor.
But for me that was like very
obvious application, right.
My thesis was and was very
challenging with under Professor
Mass Nelson and Sammy Brand was
that given an image of her
breast where the woman is coming
from, a screening right, where
there is no detection of any
cancer, nothing.
Can you find can you assess the
risk of that woman developing
breast cancer in four years from
now, two years from now based on
computer vision and machine?
So you did.
So that was really did you
computer vision then in
principle what we're now trying
to do better with the quantum
computers for digital
pathologies?
Yeah, yes, yes, exactly.
So my thesis was about
developing biomedical imaging
biomarkers and then that led me
to also went to Upenn,
University of Pennsylvania.
That was the first time I went
to actually US.
I had a really nice, nice
experience working with Upenn,
with Professor Desmina Kontos,
who is now leading in Columbia
University as a research.
I also did my internship in
Siemens corporate research where
actually learn how machine
learning becomes a product right
in Princeton in New Jersey.
So it was really nice experience
I had.
Of course then I had also a
chance to work with Mayo Clinic,
you know, Nymagon universities
and things like that.
So that was really nice things.
This was the part where I got a
really nice hold of what is a
clinical trial, What is the
patient data, you know, what are
the challenges and the clinical
trial development and stuff like
that.
And then suppose after my PSDI
went to Spain, which was again
the really nice transition from
Denmark to Spain, it was a
national centre for
cardiovascular research.
But this is the place where I
actually learned the preclinical
side, like how people are, you
know, working with cell essays,
you know, what is the knockout,
what is wild type, how the mice
model and things like that.
So it was a really nice
experience After that and the
Fast forward then I'm from
Spain, I went to Foundation
Center for Protein Research and
Stem Cell Biology.
So this was a really nice
position for me because I could
then get a hold of what is
happening in the protein
engineering at the same time
what is happening in the stem
cell biology.
That means I got to entangle
with the people who are
developing really top notch
developmental biology research
and all and protein engineering
and stuff like that.
And in principle you could, by
the way, in principle you could
even then have designed the
cells that produce the proteins
or.
Exactly.
And that's that's the whole
idea.
This is exactly what perspective
paper that we published that how
quantum can be helpful for
optimizing the motifs in the T
cells for example and antigen
receptor therapies.
So I think so this was really
interesting for me and now I
look back connecting dots.
So this was really gave me a
journey where I could now
communicate with, you know,
people from preclinical side to
CMC to the to the clinical trial
and I always correlate
everything with the quantum
computing.
What is the possibility of
quantum computing that could be
solved for the problems, the
challenge that's that these guys
are having?
So, yeah, so that was my
experience actually.
And then the final, like the job
that you referred to of the
pharma company, I was, I was
director for a department called
Emergent Technologies and
External Partnership where I was
focusing on quantum computing.
You know, that was the first
time I was actually exposed to
quantum technologies.
You know, being in Denmark, of
course, near to Nilsborne
Institute, a lot of professors.
It was really fortunate for me
to understand and learn.
I still remember my first
international with the quantum
system was when I visited a
Copenhagen lab from Microsoft,
right?
And I really see what is
happening there.
And of course there's IBM
workshops and stuff.
So it was really fascinating
place that I really learned and
I'm really thankful for Denmark
and its quantum ecosystem.
This is a fantastic system and
I, I, I mean, I just jumping off
from a from the quantum tech
conference and there there was
modern bar.
So I, I hope to get it also
maybe later on on on the show
for the Nova Foundation, because
it's quite fascinating how how
the Nordic are.
Definitely.
I mean, I just want to mention
like as you mentioned, Morton,
Bucky, Lena, Orasha, Peter
Kostov, they are the one who
actually motivated me, right?
Like okay, what is quantum
computing?
And they Peter, I still remember
he basically showed me in this
born situ, right?
And the motion was it's always
nice to talk to him like what
the Danish initiatives in
quantum computing, quantum
information science, quantum
sensing.
So it was very, very helpful for
me.
Small circles.
Correct the dots small.
Circles and then I mean, I just
recall the the time.
Maybe we can chat a bit of this.
But when I was sitting on my on
my computer, then I came an
e-mail from Gopal Calamor.
I got this invitation to write a
book chapter.
You want to talk a bit on that?
That's really cool.
And it was really, I'm happy for
this engagement that we had when
I was in the we, we had a chance
to write a, a, a, a chapter, a
whole chapter on quantum
computing for Wiley publication,
which was, I think, really nice
kind of exposure to the
community, Like what is
happening in this emerging
technologies and why people
should be exploring this
particular tool in tub
discovery.
So, so I think we for for the
listeners who are keen to read
it, I will later put just a link
in the in the comments and Gopal
tell me so this it's quite
impressive.
And now I understood how it
makes sense also in your, in
your new role leading the
quantum initiatives at IBM and
talking about your new role.
I imagine there are quite some
also wide definitions of what
healthcare and life science.
I understood is, is together.
And do you want to tell us a bit
what this means or what what
what what you're doing all day?
Right, right.
So, so my, my title is I'm a
global lead for quantum
computing and healthcare and
life science based in Yorkton
Heights in New York, which is
basically a, a Ground Zero for
quantum computing research.
And it has been there from the
century, right?
So I'm very proud and very feel
fortunate that I can go and
interact with the many big
scientists, right, which you
normally see in on a YouTube and
things.
So I was so, so I joined IBM
like almost one year ago as a
global quantum lead.
And my role is to provide a
consultancy on the, on the
C-Suite's executives, right?
You know, developing the
advisory, basically developing
the, the artefact for on the
C-Suite executives, right?
But also I have a dual role,
right, To interact with the
pharma community, right?
Understand their problem and
formulate their problem with the
quantum computing so that I can
also then interact with our, our
development team, our, our
delivery team, right?
Yeah, yeah.
So, so that was really nice job.
I'm I'm enjoying and of course
it's challenging yeah, because,
you know, you really need to
understand the two kind of a 22
hats on your head, right,
Because you you need to be very
well updated with what is
happening in a pharma domain.
What are the challenges right in
drug discovery and development
from target discovery to
molecule design to clinical
trial.
At the same time, you have to
also in order to speak with your
quantum scientists from IBM,
right?
So they they don't speak that
language.
So of course you need to also
understand their language.
That means what is shows
algorithm, what is complexity,
what is the cubits, what is
gates and stuff like that.
So yeah, I really enjoy my job
here.
It's challenging but I enjoy.
This.
Yeah.
And I think it's a fascinating
thing.
And I'm not getting tired to say
that when I was a very young
kid, I was also a bit crazy and
wanted to change the world while
I'm still want to.
But then I, I reached out and
there, there was this, there was
the, the magazines where you had
the IBM, I'm not not quantum,
but IBM computers and, and
Rushley Kahn and I just asked
IBM.
I'm so fascinated.
I was, I think 8 years old or
so.
And they sent me the full
booklet.
I mean it, it was, it was really
a meter of books.
And I was so fascinated.
It's always my dream to go
there.
And finally, of course, I, I, I
had the chance to be in wish
Recon and touch the five qubit
machine.
I mean, it didn't break it, but
I did not and play and play with
the kiss kiss stuff.
So in the beginning when it was
still quasm.
So I think it's, it's and it's
really amazing what, what the,
the development.
I think all of us also on the
other side are, are quite
surprised what's going so, so
there's big leaps in the, in the
quantum technology and we're,
we're quite advanced and we have
a lot of different approaches to
solve, let's say computational
problems being classical
computer quantum computers.
Do you have any idea or can you
share what you are consulting
maybe internally or also the the
guys like me on the other side,
what would make sense where
quantum computing could really
add value because we want to
believe, but sometimes it's
quite tough.
Exactly and totally.
So for that the reason I mean,
we conducted A webinar which is
available and just for the
listeners who really want to
know deep dive into what is the
possibility of each and every
functional areas of drug
discovery and development.
Because I strongly believe when
you say healthcare and life
science, life science is related
to understanding disease
mechanism where the Healthcare
is related to the patient data.
So the bridge between this
healthcare and life science is a
pharma company, right?
Because this is where exactly
the translational science
happened.
Translational medicine happens,
right?
OK, from that's what they call
bench to bed site, right?
You may be having a really nice
basic research and all, but
unless you put it into the
practice, like put it into the
FDA regulation, become that as a
drug available to the patient,
it's not helpful, right?
So I think for for me, this is
very important.
So, so if you really see the
functional area, there's a
webinar which is available.
I would very happy to to give
you the.
Yeah, we put it in.
Yeah, yeah.
Yeah, exactly, exactly.
But then just to give you the
the brief idea about what are
the potential application domain
within healthcare and life
sciences.
First, let's start with the, the
discovery development pipeline
means target discovery, right?
So right now the way we solve
the target discovery problem is
by, you know, understanding the,
the basic biology, the mechanism
like the system pharmacology,
systems biology problem.
And basically we generate lots
of lots of networks, right?
They're understanding the, the
knowledge graph, for example,
they're traversing over this
knowledge graph or let's say in
the, in the technical term, if
the, if the graph data structure
it is, we're still using the
heuristic approaches, right?
There's no deterministic
approach of how do I then
traverse over this particular
node, start from this particular
node and get to the reach to the
destination.
And because of that, we are not
generating the novel hypothesis
or information rich hypothesis
based on the data that we've
collected from multi omics
level, right.
So the proposal also in the
community is that algorithm like
quantum walk, could it be
helpful for for for developing
or coming up with a hypothesis
which is a completely novel,
completely efficient as compared
to the classic?
And just to, to to because I
think I have a rough
understanding of quantum, what
The thing is.
But in lay terms, could you just
say what quantum what the
difference is between normal
walk and random and quantum
right.
So, so everything boils down to
this three basic principle on
the basis of which the quantum
computing is best, right?
And the superposition,
entanglement and interference,
right?
So superposition, as as many of
you, your audience are aware
that it's basically a
superposition.
It basically gives massive
parallelism, right?
In the competition term
entanglement is massive
correlation, the correlation
that you cannot find in the
regular classical covariance
metrics or correlation metrics,
right?
And that's why we that's what
the multi referential methods
and all I will come to the
quantum chemistry side.
But what quantum work does is it
basically does the solution and
then again, the 3rd part that is
very important, right?
That's interference.
That means that how do you get
reached to the solution path,
the solution.
So you start with the
Hamiltonian problem,
Hamiltonian.
But because of this constructive
interference and the destructive
interference.
You basically reach to this
steady state solution, right?
So quantum walk utilizes all
these principles, right?
And so, for example, it can, it
can walk over multiple nodes at
the same time.
And that's like in a layman's
language for the people to
understand, right?
Other instead of crossing each
and every path, for example,
travelling salesman problem,
right?
Like, you know, basically spend
your ages right to, to, to try
each and every permutation
combination.
While the algorithms we can
actually solve that problem.
So that's, that's the whole
idea.
Yeah, yeah.
In a very layman's language.
Most of the quantum computing
companies have very good
educational material, so I
really urge the US to check also
those from IBM mode because
there are these nice
visualizations.
I think I recall the one where
you would just see a typical
maze and then one dot starts and
then you see the classical
approach with the error, trial
and error.
You go back and forth and the
other one is really just filling
everything and then eliminating
the solutions.
Exactly.
But just just to continue, I
mean this is like.
One part of 1.
Part of like a drug discovery
and development, like target
discovery.
But what we see and what we have
realised is that the important
challenge or let's say main
challenge of drug discovery and
development community is about
how to design a molecule in a
better way, right?
Because designing A molecule
from the molecular formats which
is starting from let's say small
molecule, which is very like a
small molecular weight, right?
I'm talking from the molecular
weight perspective, the small
molecule antibodies, short
peptides, cyclic peptides,
oligonucleid, these are all the
fascinating nuclear formats or
molecule formats that are pharma
companies now investing, right?
Because all low hanging fruits
are gone.
Like, OK, we can't really
develop a small molecule that
can go, you know, permeable
through the cell membrane.
So we have to really start
innovating like RNA therapy, for
example, talking about
oligonucleotides.
But you know, you can't, you
can't really go to the
laboratory, try each and every
permutation, go to the
laboratory, synthesize the
molecule, come back, test, you
know, really, really see what is
the biophysical.
Yeah, yeah.
Pharmacokinetics properties,
right.
Pharmacodynamics properties.
So we rely upon simulation,
right?
And this is this, we have been
using simulation tools from ever
since we started duct discovery,
compression duct discovery,
right?
The challenge of simulation
right now, the tools that we
have is about the accuracy.
OK, so, but that doesn't mean
that in every time you really
need an accuracy to the level of
Schrodinger equation solving
really an orbital level of
interaction between the drug and
target interaction.
But in some cases you really
need that because in order to
assure that your, your, your
molecule is binding to the right
protein and not the off target
affinity, then it should have so
to such to, to have this design
cycle into consideration as a
simulation, you have to rely
upon the high accuracy.
Okay.
And now if you see what is
available right now is like,
okay, we can go for force field,
we can go for semi empirical
methods.
We can at most go to the
accuracy of DfT DfT is like a
best possible kind of accuracy
that we can reach to right now
when the tools that we have.
But that is not enough because
we're not really solving the the
Schrodinger equation like a full
configuration interaction multi
referential system.
And this boils down to the
problem where a pharma company
who are focusing on let's say
transition metal ion based
therapy, like for example iron
sulfur cluster based kind of you
know, like whenever there is a
heavy metal involved, it's a
transition metal.
Yeah.
What happens with this such kind
of a system is that there D
orbital is semi failed with
electron, right?
And you know, SP is OK to deal
with, but when it goes to DNF
orbital, it's a really complex
orbital for electron to stay so
to.
And of course because of that
the energy level are so quiet
and similar that they have a
multi referential kind of
perspective and that you really
need to solve that one because
you know you can't really
consider electron one individual
electron as an independent 1.
So because of that the hole
boils down to an Hamiltonian.
That Hamiltonian is so complex
and it is not non sparse because
you know every every parameters
or every electron is.
Interacting with every other.
Electron exactly to solve this
problem using classical computer
and we have actually shown using
our recent paper that the
publication that we had with
Rican as a Japanese research
organization that the method
like sample based quantum
diagonalization.
How these methods in conjunction
with classical computer
supercomputer can help solve the
problem in two hours, which
would otherwise take years to
really solve only on the quantum
computer, right?
So I think this is also very
fascinating from the drug
discovery side and the molecular
discovery.
Then coming to the clinical
trial, of course, we have
published some papers on how one
can use an optimization quantum
optimization algorithm to design
A clinical trial strategy.
You know, you really want to,
because, you know, clinical
trial is also one of the really
challenging terminal options,
right?
Because you know, people, people
exactly.
I mean, you, you know, as a
pharma company, right?
Because 90% of the attrition
rate and that's really, really
bad, right?
Because so only 10% of the of
the molecule that goes into the
market, right?
So how to how to reduce the how
to increase the technical
probability of success in
clinical trial.
And I know that I don't want to
claim that we can really control
the clinical trial because we
should not be controlling
clinic.
That's why we call double-blind
control trial.
But you know, apart from
efficacy and the safety feature
which is very important for
which we have double-blind
clinical control trial, there is
also some factors which defines
the success of clinical trial.
That means what kind of patients
are recruiting for the cohort,
right?
And on the basis of what?
And that's why we're talking
about the personalized medicine.
So I strongly believe that the,
the methods like the, the
quantum control based quantum
computing based optimization
like QAOA or VQE could be a
solution for designing the next
generation clinical trials based
on the variables, you know,
where all the binary variables
and discrete variables think of.
So that's like a overall
application domain.
I can think of whenever I talk
to a Douglas development,
whenever I talk to pharma
companies, you know, because
they either either or other way
they help, they have this
problem.
Is it a preclinical problem?
It's a clinical problem.
And then of course, we, we
provide our consultancy and, and
we are, we are very honest,
right?
Because we also have to think
about there's also hype outside,
right, about what can be done on
quantum computer, right?
But I think we we are very, very
honest from the beginning that
we have divided into 4 different
application domain as far as the
quantum computing is concerned.
One application domain is called
Hamiltonian simulation, right?
Another one is a quantum
optimization, which I just
mentioned, right?
The third one which is a long
term is quantum machine
learning.
And the 4th 1 is a PDE, the
partial differential.
Equation the 4th.
Simulation, right, because if
you really see the any
engineering discipline, they all
some or the other way uses PDE
or machine learning or machine
learning or, you know,
Hamiltonian simulation.
And then as I just explained,
Hamiltonian simulation is a very
kind of a this is like Ising
model type of Hamiltonian.
If really want, if you have a
problem which can be defined as
an Hamiltonian And just for the
general public, I know that
you're a physicist, you
understand the Hamiltonian.
But for for general for the for
the lay mass, it's basically
kind of energy energy of a
system.
And you can think of energy as
energy of like a sum of all the
edges in the graph.
For example, you that you want
to minimize, right or finding
the ground state energy of a
fermionic Hamiltonian, right.
In all this aspect, the quantum
is a natural solution.
And why do I say so?
Because as I said before that if
you have a very huge covariance
matrix and it is non sparse,
finding eigenvalues and
eigenvectors are it's impulse
problem in classical domain,
right?
Because you really can't inverse
that huge Hamiltonian, right.
So the way we have solved this
problem and this is exactly what
is happening in the sample based
quantum diagonalization kind of
method that I just mentioned
before that we are very
positive.
And then we have got a really
nice result on iron separate
cluster is that you basically
sample the confirmation using
quantum computing.
So you only sample the
confirmations which are very
relevant for eigenvalues and
eigenvectors to be computed for
the classical computer.
See.
So you're not you're not
sampling the entire space, which
you normally do, right.
But it's basically you use a
quantum sampler as APMT process
and then later on you basically
solve the eigenvalues,
eigenvectors by classical
supercomputer.
Right.
So this is also very fascinating
things.
This is cool.
I mean, thanks for sharing that
good part.
And I, I think this brings us to
the struggles in real life,
because I can imagine that
certain type of people who wants
to believe but have seen a lot
of things failing, have very
good ideas why it's so super
complicated.
So, and I'm just thinking, for
example, when you were talking
about the high precision
calculations, what I often hear
when I try to convince that
quantum computing is an
additional value added method, I
often get back, yeah, but why do
I need this precision
calculations if I just have
unclear atomic positions and
they are only for 0 Kelvin and
life is more than 0 Kelvin,
etcetera?
Do we have an idea how you
answer this?
Yeah.
So definitely as I said in in my
answering your question about it
depends on what is the need,
right for a pharma company.
If a pharma company is
developing just a regular small
molecule, which you know, the
very mechanism about, let's say
GLP one targeting, right, like
agonist, for example, then you
don't need that kind of accuracy
because you have, you have lots
of biophysical data,
physiochemical data, you don't
really don't need like a docking
is OK, just just a docking.
A normal docking is OK, right?
But, and this is actually a
really nice segue to tell you
about what, what, what we're
doing with our enterprise
client, right?
And this is very important.
Again, there's a disclaimer.
It's all explorative, right?
It's just a, if those who really
want to explore from these from
the beginning about how quantum
can be helpful for their value
chain, it's only for those,
right?
I mean, of course we're not
selling like you know, so we're
not hyping it, right.
But just to give an example,
what we are, what we are working
with Cleveland Clinic Moderna
for example, and that's really a
kind of a nice answer to your
question is for example,
Cleveland Clinic, we have a 10
year partnership with them,
right?
So we're going to explore.
I think this is the biggest this
is in the world.
This is the first quantum
computer ever dedicated for
biomedical research.
We are happy for this
partnership.
So we are exploring different
angles in the discovery and
development.
But just one example that I want
to give you is about the protein
folding problem, right?
So I always get this question,
OK, didn't we solve this
problem?
Like we got a Nobel.
Prize like in chemistry and I'm
really happy with that.
Like this is what we really need
because when I was working for
protein CPR Center for protein
research, I know people spend
like months to collect the data
for structure data like with chi
OEM or crystallography and all
but alpha fold and the Rosetta
fold, simple fold, These are all
the software which people use
this right Normally now, but
there is a catch here.
If you have a single point
mutation in your protein, right?
The software which is based on
data, they cannot capture this
one, right?
They cannot tell you where
exactly the single point
mutation of amino acid is
happening.
And because of that, the
structure is changing, right?
For example, if you really want
to understand the intrinsically
disordered protein, which is
very kind of a very interesting
topic for former companies,
because not necessarily that the
receptor binding domain is the
only place where drug interacts,
drug might interacts in any
other places and even even
without docking the protein
might change the structure.
Yeah, just because it's a great
place.
Yes, we, we, we call this an
allosteric effect in pharma,
right?
And people who who can hear, who
can listen, they, they know what
is the allosteric effect.
This is unsolved problem in the
drug discovery, right.
So to understand such phenomena,
we really need a tool to go to
the mechanistic level.
That means the automatic auto in
the atom level, right?
Like interaction electron,
electron interaction, for
example.
Or if you're like, let's say, if
you're working on a drug, which
is, which has a heavy metal or
let's say transition metal,
right?
Let's say iron, copper,
manganese, for example, then we
know that these, as I said
before, these are very this is
a, this is a nightmare for for a
quantum penis.
Or let's say it's a theoretical
chemist to really come up and
understand because it's AD shell
is incomplete, right?
And that's why it is basically
very, very unstable molecule.
And it's a multi referential
system, right?
So in such cases you really need
a tools like quantum computing.
So we're not selling to each and
everyone like I'm not saying,
oh, you just replace the large
language model or foundation
model and just.
Yes, with the quantum computer.
Yes.
So I think, I think all these
tools are very nice that
they're, they're really good and
they're solving the the purpose.
But quantum is for problems,
which is beyond the capacity of
a classical computer, right.
And this is actually for the
listener.
It's also nice to know that this
is what we have done in 2000,
2023 when we demonstrated in
collaboration with UC Berkeley
our utility experiment, right?
This was a time where we have
showed that the quantum computer
can do work which is beyond the
brute force classical
simulation, right?
And all other partners like
algorithmic I can mention here,
they have taken this approach
and they have applied on quantum
chemistry and they have showed
or to error mitigation.
Look what we can do right now
when I always get this question,
OK, don't we have to wait for
2029 because you still have like
a error prone devices or quantum
computers even though they're
more than 100 qubits?
No, but you know with error
mitigation you can actually do a
lot of things.
And this is what we have shown.
And this is just the one example
that given you with algorithmic,
there are many other partners
who are actually exploring.
We have actually 300 plus
quantum computing network
partners in start-ups, we're
actually utilizing our machines
so.
Congratulations.
It's really good.
And I'd just like to shift gear
a bit because I see that you
just cannot do it without
personal values or purpose.
And I think so.
I always have you put in my in
my box of a very curious and
brave and not shy person.
So can you just go a bit on the
importance of personal values
and also maybe I mean, you're
also a parent and and and
mentorship, etcetera.
So touching these points more.
Definitely, I mean, for for me,
the purpose is exactly what
drives what are the very values,
right?
Because if, if you don't have a
purpose, then value could be
really nice.
But OK, what's the point?
And of course, if you have a
really nice purpose, then values
automatically align or entangled
with the the purpose, right.
OK, So I think for, for me, it
has been always really to
contribute to the society from
the from there, because when I
did my engineering, which was an
electronics engineer, I was
really reading the Physiology
and the biology and anatomy kind
of work.
So I always wanted to work with
this interaction or the
intersection between the, the
technology and healthcare,
right?
So that really is my purpose
that if I can be a player to
kind of a stimulate this whole
ecosystem of what are the
current challenge that the
healthcare and life science
community has through the this
innovative tool, which is a
quantum computing tool, right?
And, and I'm very fortunate that
I always get inspired and very
proud that to be part of IBM
quantum research, because all
scientists here are driven by
science.
I mean, all they are, they are
their purpose, right?
Because they really want to
solve the challenging problems.
So whenever I interact with
them, you know, listening to, to
from, from the clients, they
have this problem they are
always excited about, OK, where
can we apply quantum computing
algorithm, the present
algorithms to solve that
particular problem.
So I think that really drives
the the purpose of my life so
far and hopefully it will be in
the future.
Sure, sure.
Why Why should it change?
And I I have always the idea to
ask regarding mentorship.
So I think you're also working
with younger people just to
share expertise or be the be the
older brother or whatever.
Are you doing this at the moment
or is is it?
Is there something what IBM is
doing or how does this work?
Yeah, So, so IBMI mean you know,
the Kiskit community, yes, it's
an open source platform.
I think we are very proud that
we are driving and and it's a
most popular SDK in quantum
computing and and we are happy
that people are contributing
also they're they're they're
getting help, you know, so this
is so it's it's an open source
platform for those who are who
do not know it's it's a Kiskit,
it's a quantum information
science tool kit.
But actually you can and develop
an algorithm and we have given
lots of abstraction level,
right?
Because we know that people, not
everybody is quantum savvy,
right?
People do not know about what is
quantum, quantum mechanics.
So we have given a lot of
abstraction and the Python
levels, we actually, if you know
Python, you can actually use
quantum computer, you can
interact with the quantum
computer.
You can develop a small quantum
routine, for example, you know,
you can actually get a hands on
on what is happening in the Bell
state.
You can create a Hadamard like
AC, naughty gate and stuff like
that, right?
But also for those who are very
inquisitive about what is the
error rate, like what is error
mitigation, what can I do with
the error, you know, error
correction, stuff like that.
So we have also given the APIs
like CAPIS, you know, we have
given the Rust APIs so that you
can actually interact with the
microwave pulses, you can really
interact with the hardware.
This is, by the way, one of
these, one of these challenges I
hated during this exercises we
get just kidding.
Yeah, go.
Go, but that's how it is and I'm
very happy that PP and we also
organize summit, right?
We also organize lots of summer
school for students to really be
part of that.
They can apply for internship in
IBM quantum research here.
So all these initiatives are
very important for ecosystem and
also for the general public to
understand about the quantum
computing and it's implications
in healthcare and life science
and also other other other
application areas.
Because healthcare and life
science is not the only one all
my contract partners working on
equally well for other industry
sectors.
Before I jump to the next thing
that because I'm really keen to
see how you deal with future
tech impact and specifically as
a technology company, not only
doing quantum.
So, but one, one quick follow up
on this mentor thing, putting
yourselves now back 25 years
younger is, is there is there
something you would do different
or something you're older, you
what you know now, would you
advise to either just continue
doing or doing completely
different?
Yeah, I would say always stay
curious, right?
I mean, I have whenever you see
even kids, right, the attention
span is very limited, right,
because of all the apps and all
these things going on.
But I strongly believe that I
think you have to be persistent,
right?
You have to be very focused
towards what you really want.
And this if somebody comes to me
and say, OK, what, what, what
should I do to be successful in
in such a dynamic technology?
I would say stay focused, pick
up some technology.
I mean, for example, let's say
quantum computing explorer,
right?
We have lots of tools available.
I mean, cascade is something
which is available, right?
I mean, you just install it.
You can have it available online
from Chrome, Python, you know
very well, right?
And more important is that if I
tell people, please, there are
lots of resources available free
on, on Internet.
Like I just want to mention
highlight here, we have really
nice lecture series from Olivia
Lens, for example.
So if you really want to
understand where exactly is what
is quantum mechanics, what is
the history of quantum
mechanics?
What is the history of, you
know, the quantum computing
science, She will explain you
very well.
So I, I mean, when I was growing
up, I was, I had to go to the
library, really go to the book,
really understand and read and
all these things.
So I think and also there's a
lot of resources available.
I mean a lot of YouTube videos
are there and everything.
So I think this is this is
really nice.
And I just want this.
Is what I would recommend.
And I think it's really cool to
just say play.
And this brings me to the guy
who James Wooden, I guess you
also know who did play to the
fullest extent.
And I was doing just other stuff
in the creativity industry.
And I think that's that's really
very good advice just to start
playing right.
And just for the, for the people
who, who are inquisitive or the
youngsters, right, because they
always goes by by LinkedIn and
all this thing like a, like a
celebrate their victory.
I mean, we have a certification.
So you, you, you complete your
course, you get a certification
from IBM Quantum and that's
really nice.
It's a kind of a pleasure for
people to to have.
I'm proud to share this.
I'm proud to share this exactly.
Yeah, that's that's good, right?
Black Badge.
I'm shifting gears again a bit
future tech impact.
So I think you've seen a lot
from both perspectives.
And I think we also have seen
and you mentioned the interface
between different domains.
It's just say quantum computing
and AI and drug discovery and
healthcare and everything is
running so fast that we
sometimes maybe lose a bit track
or or they are emerging
synergies or emergent properties
we just would not have thought
of.
Do you want to dare a bit of a,
let's say at least 5 or 10 years
in the future outlook how
technology could support and
what we maybe should not do?
Right.
And this is a really nice segue
where I really want to share the
the region of quantum computing.
We strongly believe that the
future is going to be quantum
centric supercomputing and if
you see IBM perspective that
when we get this question of
where OK, is quantum going to
replace classical computer, no.
Never, right?
I mean, at least not.
Now so but but quantum would be
and one of the the member of the
high performance computing
framework wherever you go in
future, right?
That's what we call as a quantum
centric supercomputing, where
quantum computers will work in
tandem with your supercomputer
or GPU's or whatever you talk
about the AI based machines,
right, To uplift the performance
of classical computer.
Because for me, the user
feedback or user interface has
is always going to be classical,
right?
I mean, you're not gonna see the
genomics on a quantum computer.
You're always gonna see on a
laptop on a classical computer,
right?
Yeah, if you really want to
align the sequences and all
stuff like that.
So, but in the background you
can be having a jobs running on
a quantum computer.
It could be from FPGA, it could
be from GPU's, it could be
anything.
So I would strongly say that
it's quantum centric super
computing is very, very
fascinating for me.
And this is actually the IBM's
kind of a vision and I'm very
happy that we are working
towards that.
Just to tell you what we have
done so far, it's not something
that's an idea.
It is actually happening right
now, just to tell you not like
100 miles away from where I'm
sitting now.
It's Rensselaer, Portingley
Institute, RPI.
This is really a premier
research organization and
university.
So we installed a quantum
computer which is actually
communicating with with, with
the classical computer super
computer, right?
And we are doing a lot of a lot
of fascinating experimentation
on that.
That's one example.
Second example is with the Rican
where the quantum computer is
actually sitting besides the
supercomputer, which is the 7th
largest supercomputer.
And if you ask me what exactly
we're doing, we're actually we
have done some experimentation
such as iron separate cluster
simulation using sample based
quantum.
So this is something is
happening right now.
You see, this is like just the
start.
This one.
So if you really see a road map,
which I did not have a time to
talk about, but it's available
in the public domain, yeah.
So if you really see what is
happening in next couple of
years, by 2029 we're gonna have
a logical qubits, like 200
logical qubits and the millions
of gates.
So you can just imagine what is
the possibility, right?
How complex and complicated
algorithm you can actually put
it in a in the unity operation
on the this two qubit gig
operation, right?
And with a logical qubit like a
like a large scale fault
tolerant quantum computer.
So it's just the beginning,
right?
And then so for me, it is for
people to understand that if
they really want to prepare for
the future, and this is what I'm
talking about the, the, the next
generation like all this Gen.
Z's, right, they should really
explore right now.
And that's, that's available.
Of course, 10 minutes is free,
right?
I mean, they can always
subscribe for 10 minutes for IBM
access.
But later on, there are so many
other engagement partnership
that we have and I will be very
happy.
I recall you even have 4-4 very
promising projects there.
There is this idea of
collaboration.
So maybe we can talk later a bit
of, of the collaborations
models, because one thing I want
to check with you because I
guess you know me, but the, the
challenge we have currently in
formulating our problems so that
we can really make use of these
quantum features, so
superposition, etcetera.
So I mean, we have a few minutes
ago you mentioned the quantum
random walk, which definitely
makes sense.
Then I, we, we have these usual
suspects like Shaw algorithm or
what have you, where very
specific algorithms.
But do you see that we need to
help mankind or everyone to
formulate eventually even health
problems in a kind that it's
naturally fitting to be crying
for a quantum solution?
Because I have a bit the feeling
that still we try to do digital
computing, but just with a
quantum computer and using
quantum.
Yeah.
So or eventually generating
sensor data, quantum sensor,
real quantum data out of the
person, I would not sure, I
would not know how to do that
because after measurement you
always have classical data.
But any ideas or am I just
crazy?
Yeah, definitely.
And I also completely agree, I
think that the biggest problem
or the challenge that we have is
these IO operations, right.
I mean, OK, it's really nice
that you have a parameterised
circuit, right or the weekly
approach.
But this the time it takes to
convert or encode that data from
classical to to to quantum, it's
a, it's a big process, right.
And this is very cumbersome some
time, but as you mentioned just
just before, it's about the
feature mapping, right?
So let let me talk a little bit
about also.
And now I'm scratching the
surface of quantum machine
learning.
So OK, even though it is our
long term vision, but and we we
published papers in both, you
know, the supervised machine
learning, quantum machine
learning, unsupervised quantum
machine like quantum sampling.
What is this quantum Boltzmann
sampling would look like?
How it would generate like AQ
Gan and stuff like that, OK.
But our as you can see, the
IBMIBM strongly believes in
proofs, right?
I mean, that's, that's how I
mean, whenever I interact with
the scientists, that's also
they're driven by science,
right?
So, so far we did not have a
evidence which would tell OK,
for this particular case,
quantum machine learning would
always perform better as
compared to the classical
quantum part.
OK.
So, but I would say that there
are two things in the, in both
classification and regression. 1
is your, your, your aim is to
find your accuracy better as
compared to the classical
counterpart.
Or second one is that can you
represent your data in a very
efficient manner before you even
feed it to the classical
algorithm?
It could be supervised or
unsupervised.
Right.
You mean this is exactly quantum
feature mapping?
Yeah, exactly.
Quantum feature mapping, you
see.
And this is where I think I
strongly believe.
And then maybe I just want to
highlight here that we recently
presented a result with HSBC.
This is actually the 37% or 33%
improvement in the one
prediction rate.
And this is exactly what we've
done.
What we have done is we have
basically feature mapped.
We basically encoded our data,
the classical data into the
quantum feature map.
And we have shown that even if
you use a classical routine,
which is by the way, we can't
change and we can't touch
because of the regulatory
authorities and stuff like that,
both in the financial same like
this FDA, right.
So without changing the
classical part, if you just
represent data in different way,
there's a benefit there.
However, we, we are saying that
we don't know how it is working.
We also tried and tested against
the classical, classical noise
that you can generate like the
pseudo noise, but even the
quantum was performing better,
right.
So here's the case where I would
say that we have some
preliminary result, but it is
not, it is still it's an active
area of research and.
This is a very good point
because I, I think you recall
the idea that finally we did
without you because you stepped
to this other company just for
our listeners, we, we had tried
to understand what quantum
reservoir computing is.
And in, in the end, it's also
using quantum technology to
encode your data and, and just
encode it in a different way.
And also we see that for few
data, it's performing better
than for less bad than classical
machine learning.
And I think this is really super
cool also that we just walk
different path, but everybody's
somehow coming to this.
Oops.
There seems to be something that
works better, but we just don't
know how and why.
And I think this is really
super, super important also.
And I think that's coming back
to the curiosity that you
mentioned earlier as as just
exactly.
Your drive, but that's why we
see lots of interest from the
university academia, because
this is the place where people
explore new technologies, right,
without expecting much of the
result, right.
But at the same time, those
industries who are like a long
vision, do they have a long
vision, right?
They, they also explore such
tools and quantum.
This is where exactly we are.
We are very happy to engage with
our enterprise client.
They come to us with a with a
really fascinating kind of a
problem or the challenge they
have in the value chain, and we
try to formulate the problem
onto the quantum computer.
Yeah, and I think we touched
pressing problems.
In principle, everything that
makes sense could be addressed,
just just we don't know how and
but, but what I'm still want to
learn or I think it's, it's
always interesting also for the
listeners is innovation
strategy.
So I mean, do you have you
personally?
So how do you cope with it?
Because I'm honestly currently a
bit overwhelmed with all the
publications and the AI slot
generated on top of it.
And and then writing your AI
machine, it's reading the papers
for you and writing summaries
and it doesn't really satisfy
myself.
So because I didn't read it
myself.
Do you have any personal
strategies you might want to
share that work that don't work?
I, I would suggest just go to
ibmquantum.com or
ibm.com/quantum, you know, and
learn look like Kiskit, the
videos which is available.
Sometimes it's free, sometimes
you have to pay, for example,
just just to give it like if you
really want a Kiskit developer
certification, it costs 200 U.S.
dollar, but that's like a worth,
right?
If you really want to looking
for a, a job in the quantum
ecosystem, this certification is
really important.
And this is being evaluated by
the personally, by the by the
people who are expert in this
particular domain, right?
But there are so many free
videos which you can also have a
look at, right, to understand
what is happening.
And as I said before, also the,
the summer school, you know, the
having a like a university tech
summit, right?
And the quantum is almost
everywhere these days, right?
There was a time when people say
it's over hyped and all, but if
you just go look at any, almost
all the developing nation, the
developed nation, they have
started having their quantum
strategy.
So it's it's not a dream
anymore.
It's basically, yeah.
The question is OK, when you're
going to realise it for a
practical application to really
industry prone problem that
could be debatable and that goes
by from problem to problem.
And this is exactly what our
department is like.
We help then for those who
cannot understand how to, how to
get help from quantum computing
for their problem.
This is where exactly our
department comes where we then
say, OK, if you really want to
solve your problem, let us have
some engagement.
And I just want to tell you
about the engagement.
We have a quantum accelerator
engagement where a industry like
pharma industry can actually
engage with us.
We can give them the whole
consultancy, the whole one use
case.
We will execute with them their
team for one year, right,
because there's a fee behind it.
But we also have some smaller
engagement like quantum for
enterprise just to educate the
whole enterprise about what is
the implication of quantum
computing for their value chain.
So we have so many engagement
kind of a platform, but I would
be very happy to give you the
links later on.
Yeah, just.
But just to give you also the
idea about some innovation like
because some incubation people
or let's say some start-ups
ecosystem.
So we also have a very nice
program and then start up
ecosystem building, right?
We also have a working group,
working group within the
different industry sector, like
for example, healthcare, life
science, we have working group.
So if you are a university, if
you are a start up and if you,
if you're willing to contribute
enough, you can be a part of
this working group.
So you'll be called for the for
the workshop you can actually
get and this is actually very
kind of a no, no string attached
to that.
Way, Yeah, yeah.
And I think we definitely will
share or at least point to also
how how outreach programs work
and how startups and
universities etcetera can can
work with you guys because I
think IBM is long enough in the
game to know what's at stake.
And like like everyone, I think
everybody has good approaches.
And what I understood between
the lions, Gopal is that your
personal innovation strategy is
in principle focusing on one
thing and then just bite and
don't let go.
Is this right or?
Well, I mean, that's, that's a
sales strategy.
I don't know about the sales
strategy, but I think I think
for the quantum, I'm strongly,
I'm strong.
I mean, if you see our road map,
I strongly believe that.
And if you see our Rd.
innovation road map, by the way,
there is a 2 road maps, like 1
is a development commercial road
map and one other is a
innovation road map.
And, and we are very upfront and
transparent to, to the public,
right?
That it's, it's, this is what it
is, right?
I mean, we have a plan for for
this year, which we demonstrated
that there's a quantum centric
supercomputing can be possible.
The next year we're going to
demonstrate quantum advantage.
This is very exciting for the
people, right?
Next year onwards like 2027,
we're going to demonstrate
viable path to error corrected
quantum computer.
In 2028, we're going to
demonstrate the first error
corrected quantum computer,
right?
And in 2029 we're going to say,
we're going to demonstrate this
is a logical qubit, the 200
logical qubit you can imagine
and it's and the and the and the
millions of billions of qubits,
the two gate operations that you
can have.
And later on in 2033 with a blue
Jay is gonna be 2000 logical
cubits.
So you as a scientist within
this field can imagine the
implication of this one.
I'm talking about two to the
power 200 or 2000, the quantum
Hilbert space.
You can.
Think of and I think it's, it's
really good to educate also than
the other side who wants to use
these to to formulate the
problems because I, I mean,
right, it always is the
question.
If now somebody comes and says,
here's the here's the, the API
and a key for the full access
fully error corrected 200,000
billion logical error corrected
qubits right here you go.
I would not know what program to
run.
No.
And just one thing that if I may
then add to this particular
part, because I just spoke about
the hardware, hardware
innovation, right?
Like how we increase it.
But you know, there is something
also we also expect from the
community, like the community,
like the healthcare, life
science that you know, the
advantage or innovation in the
quantum will not come from IBM
because IBM is not a pharma
company.
IBM will never be a pharma
company, right?
So we really need a partner,
strong partner who comes with a
problem so that we from
technology side can solve that
problem, which will ultimately
help the the humanity, right?
And this is exactly where we
need also development of quantum
algorithms.
And this is our our team from
Sara Sheldon's and others are
really working hard to develop
a, a next innovation in the
quantum algorithm.
Can we go beyond VQE?
Can we go beyond QAOE, right.
Can we go please?
Something.
You can explore exactly exactly
so this is a very required and
also to decide right And we have
a 25.
In fact, start-ups who actually
be a part of, as is a, are a
part of our quantum ecosystem
who are contributing from their
side.
Just to give you 1 idea that
let's say if there's a pharma
company who may not want to join
us, like they want to be a part
of the QA or whatever the
partnership that I said.
But if they just want to explore
the quantum chemistry side, then
we have given a Kisket function.
So they can just pay to the
third party, which is
algorithmic for example, and
they can actually do their
quantum chemistry on Kiskit and
they can utilize the quantum IBM
41 bit.
So we have this all kind of
abstraction level and engagement
level that we have.
Yeah.
And this is I think also worth
mentioning then on the on the
Kiskit side, so Kisk for for
those not in quantum computing.
So Kiskit is one of the
languages, maybe the one of the
most spoken quantum computing
languages, which is really
exploding as we as we speak.
So every time there are new
features and also start-ups, as
you mentioned provide functions.
And I think it's it's really
interesting just to to check it
out.
Yeah.
And I'm just looking at the
time.
So we're closely to an hour ago
by the time flies quick.
And what I have mentioned in the
beginning also entanglement also
means to entangle with our
listeners.
And this means every listener in
principle has the chance to
submit a question.
Some do.
There's always room for more,
but some do.
And then I'm there comes this
moment where I play the Jingle.
So you will hear the Jingle and
then I press shift enter on my
on my notebook and it's running
normally on an IBM machine.
It likely will be brisk pain
again.
And normally the queue is not
too long.
So let's just see.
What?
It's it's, it's, it's just need
to tell you because it really
improved.
So sometimes I waited hours in
the queue, but currently it's
quite quick.
So and I just started and then
let's see what's coming up, OK?
OK, so do you still hear me?
You still hear me?
So I it it just it just did run
and then luckily it just took
9.3 seconds.
Excellent.
Yeah, wonderful, wonderful.
Brisbane.
Right.
Yes, I've been, I've been in
Brisbane and I have, I have a
wonderful circuit which is
consisting of eight qubits and
each has one harder met gate and
a measurement.
So it's not really that I need
fully error correction, but it
did, it did come up with
something and it's it's I'm just
going to read it.
And I think this is always
interesting to see what's coming
when we're doing this live
question.
So the, the number is it's
reading #170 so which is not
making sense.
But in the end, it's, it's, it's
from Monica from Mexico City.
Greetings to Monica.
Is there a piece of technology
from history you'd love to bring
back into modern life?
Should I repeat or?
No, I think I got it.
I think I got it right.
But but I think that from the
technology perspective, because
it's a scientific field, right?
So we have always evolved our
understanding and it is always
based on our previous
understanding, right?
That's how the the science
technology.
Because if I talk about OK, the
first microscope right, then now
we've got super resolution
microscopy, we have single
photon microscopy and things
like that.
So we have always been evolved
and we are very thankful the
technology that people have
developed, our ancestors have
developed or our previous
scientists have.
Developed on the shoulders,
yeah.
Exactly.
Building on the having a
foundation, I mean I still
remember 2016 was the first time
when we basically launched the
first time a cloud quantum
computer access with five.
I was only in 16.
It feeds like ages.
OK, good.
Exactly because it's going 16
exactly and then now you can see
you you can have access to 100
plus queues on a cloud access.
So, so we are always thankful to
the previous technology,
previous innovation.
And if the, but, but really
answering that question, can we
bring back, is there anything we
can bring back the technology or
any kind of early?
So I would say curiosity, right?
And then having the
collaboration, the collaboration
is very important, right?
These days, because I know we
are, we are obsessed with, with
our profit interest and all
those things.
But those days they were like
all the curiosity that they are.
If you really see the Solvay
conference, by the way, I was in
Belgium and I just got a picture
where exactly.
And this is exactly, I mean,
everybody is a quantum
scientist, everybody was Nobel
laureate, right?
I mean, this is, but they, they
really wanted to solve 1 puzzle
and mystery, right?
And also I, I think this is a
very nice thing, right?
Even though there was an APR
paradox, right, with the
Einstein, Peroski, Rosen, it was
it was a very healthy debate,
right, which would normally miss
these days in the scientific
community.
You mean constructive?
It's all about.
Yeah, I think we need a free
debates.
We need a debate based on the
fact, based on the scientific
evidences than other factors,
right?
You know what I mean?
So I think this is what I would
bring back in the scientific
community about the
infringement, right?
Lot of malpractices these days
we had.
I mean, I still remember when I
was in university, we have been
told to go for this course of
the how people can change their
data, manipulate their data,
right?
And I bought to the university
people the statistics class,
yes.
So I think this was not there
before.
I mean, people used to publish
papers with a good intent that
they want to solve the problem
for the community, right?
This is what actually I would
bring back the scientific
discipline in the community.
This is cool.
That's a very good point.
Yeah.
And Gopal, so you have, you have
as a guest also, of course, the
chance to use the opportunity to
contribute the question.
And I'm, I'm doing this because
sometimes when you're on the
other side of the mic and not
doing the interviews, but you're
a guest on the on the thing.
And then you wanted to talk
about something and it just a
stupid host didn't ask them
questions.
So please go.
Yeah.
I, I always myself ask this
question, right?
I mean, we are now approaching
to the technology such as, you
know, we have a large language
model, we have foundational
model, AGI and quantum
computing.
These are all tools, right?
Tools for betterment of human
life, right?
But tools without data will not
help.
And when I, when I, when I talk
about the healthcare and life
science perspective, people are
a bit skewed, especially if you,
if you see the, the, the grants
and the fundings and all they're
going towards building tools,
you know, having innovation in
the tools and the product and
things.
But we are kind of missing the
fact that this the, the funding
for basic research is very
important, right?
And this is my question is that
how do you balance this
particular part?
In one part we are developing,
we are innovating tools and all
things, but we also have to, we
also, we also need our next
generation to work on the basic
research.
That means by betting things,
you know, transcripting cells,
you know, understanding
replication, the transcription
translation, you know, all the
central dogma of life, the basic
research.
Because this is the place where
which will solve the challenge
that we have right now.
Because you really want to
solve, okay, how do I solve
antimicrobial resistance?
How do I solve my cancer?
How do I solve the problem of
the gene therapy, like cell
therapy?
Of course we need tools like
quantum computing for
simulation, AGI and stuff like
the the large language model,
but we need people who can work
on this so so my question is how
do you balance?
OK.
So the question is how do you
balance Gopal?
How would how would you answer
your question?
Oh how?
How?
OK, no, no, it's good.
So I put it in the pool and now
you.
If you really see now all the
Gen.
Z's, they want to go for
computer science, you know,
because of all the fascinating
things are happening in there.
I think we really need people
for chemistry, for theoretical
chemist, for organic chemist and
iconic chemist.
This is cool.
I like the idea.
I'm with you, OK?
Thank you.
And this brings us in principle
already to the close of our
recording.
And thanks for being my guest.
But I, I always love to have my
guest the last word.
So I'm saying goodbye and let
you have the last word.
See you soon.
And I thank you before, of
course.
Thank you very much.
Thank you.
Thank you, Thomas.
Of course, I mean, I, I would, I
would conclude with the just a
small statement.
I mean, as I said in the
beginning, biology, if you
really want to understand the
healthcare, you really need to
understand the biology, the
basic principle of biology.
And if you really want to
understand the mechanistic way
of, of how disease progress,
what it should be the cure, you
have to understand the biology.
And to understand the biology,
you need to have a chemistry
understanding.
And we know that the chemistry,
the basic principle of chemistry
is governed by physics and that
too in physics, not the nuclear
like the Newtonian physics, but
of course in the atomic level,
it's a quantum physics, right?
So to really understand the
quantum physics level of
interaction such as electron,
electron bonding, forming and
forming, you really need a tool.
And that tool is a quantum
computing.
So I'm very positive about if
you really want to solve the
challenging problems in
healthcare and life science,
quantum computing tool could be
a beneficiary for the society.
Thank you.
Yeah, dear friends, we are now
entangled.
Thanks for listening and for
being part of this experiment at
the intersection of quantum tech
and health.
If today's conversation sparked
an idea, a critique, or
triggered a question, please
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And maybe your question is
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Until next time, stay kind, stay
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unseen with untangled health.