March 10, 2025 • 12 mins
The evolving landscape of quantum computing
In a recent episode of the Energy Transition Talks podcast, Maida Zahid sat down with quantum computing expert Curtis Nybo to explore the challenges, benefits and future of this emerging technology. In this second instalment of the conversation, Curtis moves from theory to practice, focusing on energy optimization, logistics, cybersecurity and business applications for quantum computing across industries.
Overcoming challenges: The race to stable qubits
Quantum computing faces several hurdles, with coherence times being one of the biggest. Qubits, the building blocks of quantum computers, remain stable only for a limited time before randomly collapsing into a 0 or 1 state. This leads to unreliable results, making error correction and shielding from radiation critical in quantum hardware development.
Another challenge is the limited number of qubits available today. While large-scale quantum solutions require thousands of qubits, the most advanced machines currently operate with only a few hundred, restricting their problem-solving capabilities.
Optimizing supply chains with quantum computing
Quantum computing is revolutionizing supply chain logistics and optimization. Curtis highlights that quantum annealing computers are being used to optimize complex logistical processes, such as:
· Route optimization – Determining the most efficient way for delivery trucks to distribute fuel or goods while factoring in weather conditions and constraints.
· Demand forecasting – Predicting where materials need to be and at what time to prevent supply chain disruptions.
· Electricity distribution – Although not yet implemented, quantum computing could optimize energy distribution as effectively as logistics routing.
"From optimizing supply chains to predicting energy demand, quantum computing gives us the ability to solve problems we never could before." – Curtis Nybo
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Maida Zahid (00:04):
Hi everybody.
Welcome back to another episodeof the Energy Transition Talks
podcast.
My name is Maida Zahid and I'mpart of the marketing team here
at CGI in Canada.
Today we will be revisiting ourconversation in quantum
computing, and I'm joined by ourCanadian expert, curtis Naibo,
who is our lead for quantumcomputing here in Canada and
also leads some of our AI anddata analytics practices.
(00:26):
So over to you, curtis.
Curtis Nybo (00:28):
Hi everybody.
There's a lot of challengesthat come with quantum computing
, one of which is there's what'scalled coherence, and so we
have set coherence times that aqubit can remain stable before
it automatically on its owncollapses to a state of zero or
one where we want to be able toinduce, we want to be able to
measure that state on our own,kind of a chord, and not have it
(00:51):
just collapse on its own, andthat's called coherence times.
And so we're right now we'reseeing short coherence times
where qubits only remain stablefor a certain amount of time
before they just randomlycollapse a 50, 50 chance of
becoming a zero or a one whichleads to pretty not great
results overall.
And so when it comes to tryingto handle that, handle that
(01:11):
decoherence, that's one of thebig areas that there's
challenges, that they're tryingto be able to increase that
error correction capability, beable to essentially have quantum
computers produce more accurateresults.
Because right now it's prettyprone to noise.
Any bit of radiation that isable to get through to the
(01:34):
quantum chip can actually flipthose bits randomly, so they
need to have a lot of shieldingto protect against radiation.
There's a lot of differentaspects around the actual
physical qubit itself, but alsothere's only so many qubits
available right now on quantumcomputing chips.
So to be able to solve a lot ofproblems you need a thousand
plus qubits, where right nowsome of the top quantum
(01:55):
computers, depending on whatarchitecture they are, have, you
know, 50 to maybe hundreds ofqubits, while you know quantum
annealing computers essentiallyhave a little bit more, but it's
kind of it's limited by howmany qubits are available for
each hardware and processing.
Maida Zahid (02:13):
Before we move on to a little bit more challenges,
I kind of wanted to understandsome more benefits that you know
we talked about earlier.
So going beyond just energyutilities, let's say supply
chain.
So you talked aboutoptimization earlier.
So how can we kind of dive intothe benefits from that
perspective in, let's say,supply chain logistics?
Curtis Nybo (02:33):
Yeah, so that's actually the area that I spend
most of my time is aoptimization route.
So we're usually using adealing quantum computer to be
able to optimize processes fordifferent clients and solve
different problems.
And when it comes tooptimization problems, which is
usually around logistics andsupply chain, in a lot of cases
(02:57):
that includes different areaslike fuel procurement,
electricity distribution,infrastructure maintenance,
anywhere where you're actuallyhaving to plan.
So a common one is routeoptimization.
So the traveling salesmanproblem if we have so many fuel
trucks that need to makedeliveries around the world say
we have a fleet of 300 trucksand there's potentially 10,000
(03:18):
or say 2,000 fuel depots thatneed fuel delivered to, what's
the optimal way to be able toschedule that fleet of trucks to
those those uh depots?
And you can take into accountdifferent uh different variables
as well, like weather, and sothat becomes a challenging
problem for classical computersto be able to run efficiently.
(03:41):
And that's a common optimizationproblem that quantum annealing
computers especially are reallygood for solving.
Another one's demandforecasting.
We've done quite a bit of workaround predictive analytics, so
trying to plan where certainmaterials should be at a certain
time.
It's very similar to the routeoptimization problem, but
(04:04):
overall when it comes to energyand utilities.
Electricity distribution is aninteresting one that I've
thought about, but we haven'thad a chance to implement it.
But it would be a similaroptimization problem to that
route optimization.
Maida Zahid (04:21):
And I think it definitely goes beyond energy
utilities too, but a very commonchallenge, absolutely just like
cybersecurity.
So we know cybersecurity isprobably one of the biggest
things, just along with supplychain, that we hear all sorts of
organizations talk about ordeal with.
So how can we use quantumcomputing for that, or the
benefits per se?
Curtis Nybo (04:42):
Yeah.
So cybersecurity is aninteresting one, and that's
probably where most people hearabout quantum computing, because
right now, the currentencryption method for pretty
much everything on the internetis using RSA cryptography, which
is essentially trying togenerate huge prime numbers,
multiply them quickly togetherand then to be able to crack
(05:04):
that encryption you need to beable to factor what prime
numbers were multiplied tocreate the larger number, which
is a very hard problem to solve,and right now it can be.
A good thing about it is if youthink of it as a password, that
password can be.
If computers got more powerful,say twice as more powerful
tomorrow, all we'd have to dowith classical computers to make
(05:26):
it harder is add another digitto it, make it, make that a
little bit longer and it becomesin like exponentially more
powerful, whereas quantumcomputers they're.
It's now with quantum computersthat factoring of large numbers
is is very possible using somealgorithms like shore's
algorithm, which has been aroundfor a long time.
They've never been able toreally test it until quantum
(05:48):
computers actually started tobecome feasible, and so an
algorithm like Shor's algorithmnow allows essentially for the
factoring of those large numbers.
So you can essentially take anRSA key, find out what those
keys were that were multipliedtogether to create it, and then
you've essentially destroyed alot of the RSA cryptography that
exists today, and so this posesa serious threat to the usual
(06:11):
classical cryptographic systems.
The good news is that itdoesn't exist yet.
No one's been able to.
A quantum computer, essentiallywith enough qubits to be able
to run that algorithm doesn'texist yet.
But the scary part is and Ithink what most organizations
are worried about is that datacan be stolen today that's
encrypted, and then thieves justhave to hang on to that data
(06:34):
and say, maybe five years, 10years down the road this becomes
possible to be able to crackthat RSA encryption.
Now they can get that data.
They can then unlock that datalater on.
Even though the data might beout of date, it may still have,
you know, quite a bit ofpersonal information.
People only have socialsecurity numbers, for you know
their whole lives.
So it doesn't matter when thatdata is stolen in the future.
(06:55):
That data might be able to beexploited in the future.
And so the other side of it isquantum computing does provide a
bunch of different ways tocreate quantum resistant
algorithms.
So there's things like quantumkey distribution, where we're
using quantum mechanics tocreate essentially an
unbreakable encryption.
That's quantum proof, butthere's also some kind of
(07:18):
standard classical ways tocreate quantum resistant
encryption as well, so thingslike lattice-based cryptography.
There's a few common metrics,there are methods that are being
explored today, and so I thinkin the future what we'll see is
a bit of a change and a moveaway from the current framework
that we use for encryption intoa more quantum, safe encryption
(07:39):
going forward, but I thinkthat's still quite a few years
away a few years away.
Maida Zahid (07:48):
For when you're, let's say, when you're talking
to clients and trying to putthese benefits into layman's
terms, what are some of the keypoints that you come across and
you talk to, like, let's say,business focused clients that
quantum computing can deliver?
So what are the benefits thatyou are most commonly talking
about among these industries?
Curtis Nybo (08:00):
The most common benefits we're talking about
when it comes to energy andutilities is usually that
optimization type of that arecurrently running models that
are taking in a lot ofinformation.
It's taking maybe hours or daysto run a single run of that
computation.
That's a good candidate forquantum computing, especially
for quantum annealing, and so werun into that quite often and
(08:25):
that's usually where we spend alot of our time.
It also there's also a lot ofuse cases around, like we talked
about cryptography.
But overall I think what we'reseeing is mostly around the
optimization front with quantumcomputing, and so lots of, lots
of organizations are findingjust that current computation
(08:46):
can take a long time.
Quantum can potentially speedthat up, and it's important to
note too that a good problem forquantum optimization isn't one
that has just a huge amount ofdata.
That's not necessarily whatwe're looking for.
We're looking for a lot ofproblems that have a lot of
variables.
So going back to, like ascheduling problem, a large
(09:07):
fleet of trucks and also a lotof constraints.
Those trucks have workers thatcan only work between eight to
five.
They can't run at night.
They have to use a certainspecified amount of fuel.
So we're looking for problemsthat have a large set of
variables and a large set ofconstraints, and then we can
(09:28):
basically find that minimumenergy state of that problem by
minimizing that objectivefunction, which is, you know,
all those variables andconstraints, and try and find
the most optimal solution tothat, to that result.
And so what that would looklike is we'd end up with a
somewhat of a schedule thatwould tell you know which trucks
to go where, at what time andwhat the best routes would be to
(09:50):
follow.
So it's not necessarily that wewant a lot of data, like I said
, um, lots of times, it's theproblem itself.
Uh, can needs to be able to beformulated into a quantum
problem, which is usually thatLots of variables and lots of
constraints.
Maida Zahid (10:06):
I think we covered a lot.
You know sample use cases,challenges and looking ahead.
How do you see this kind ofscaling for energy utilities, or
how do you see this coming?
Where do we stand now?
It seems like we're pretty inthe early stages.
How do you see this coming?
Where do we stand now?
Like it seems like we're prettyin the early stages.
How do you see this movingforward?
Curtis Nybo (10:23):
So, moving forward, there's still a lot of work to
do.
Like I said, I think I talkedabout a few of those challenges
before, and mainly around thingslike error correction,
increasing coherence times,basically stabilizing those
qubits for longer so we can getbetter measurements when we
measure them, which results inbetter accuracy for our solved
(10:43):
problems.
But overall I think, lookinginto the future, we'll see
advancements in the quantumhardware.
So I talked a little bit abouthow a lot of problems today,
like the cybersecurity problemwith Shor's algorithm, can't
really be used today becausethere's just not enough qubits
within the quantum hardware tobe able to solve that problem.
And I think we'll start to seeadvancements in quantum hardware
(11:05):
.
So Google released their newquantum computer.
We're starting to see moreplayers into the game.
We're seeing IonQ, regetti.
They're big quantum providersand overall they're racing to
try and see who can basicallyachieve quantum supremacy the
fastest.
So which computers, whichcomputer architecture can solve
(11:27):
problems faster than a classicalcomputer is what everybody's
going for, and so we're startingto see a lot more advancements
and I think those advancementswill start to really pick up.
Another area that we're seeingexpand is the access to quantum
computing.
So for those wondering how toget started with quantum
computing.
It seems like it might be farout there, but it's actually not
.
Quantum computing is availablethrough most cloud platforms.
(11:48):
So Azure, quantum services, aws, bracket, even a lot of the
quantum providers, like D-Wave,have their own cloud provider
cloud platforms and so you canget in and actually utilize
these quantum computers to helpsolve those problems or even
just to experiment.
You can start using that today,so the barriers to entry, I
think, will also drop quite abit.
(12:10):
Hopefully.
It gave a bit of an overview ofquantum computing and some of
the capabilities.
My advice would be to kind ofjust dive right in.
Like I said, a lot ofcapabilities are available.
A lot of the places we spend alot of our time is just
experimenting with, trying toshape different problems into
quantum capable problems andthen we try and solve them using
(12:33):
quantum computers.
So the technology is here,despite what you might be
hearing, and I said, anybody canaccess it today.
Thanks so much, curtis.
Maida Zahid (12:39):
Thanks might be hearing and like I said, anybody
can access it today.
Thanks so much, curtis.
Thanks for joining us andthanks for listening everybody.
Please subscribe to our podcaston Apple Podcasts and Spotify
or wherever you get yourpodcasts from.
Thanks for joining us.
Hi everybody.
Welcome back to another episodeof the Energy Transition Talks
podcast.
My name is Maida Zahid and I'mpart of the marketing team here
at CGI in Canada.
Today we will be revisiting ourconversation in quantum
computing, and I'm joined by ourCanadian expert, curtis Naibo,
who is our lead for quantumcomputing here in Canada and
also leads some of our AI anddata analytics practices.
(00:26):
So over to you, curtis.
Curtis Nybo (00:28):
Hi everybody.
There's a lot of challengesthat come with quantum computing
, one of which is there's what'scalled coherence, and so we
have set coherence times that aqubit can remain stable before
it automatically on its owncollapses to a state of zero or
one where we want to be able toinduce, we want to be able to
measure that state on our own,kind of a chord, and not have it
(00:51):
just collapse on its own, andthat's called coherence times.
And so we're right now we'reseeing short coherence times
where qubits only remain stablefor a certain amount of time
before they just randomlycollapse a 50, 50 chance of
becoming a zero or a one whichleads to pretty not great
results overall.
And so when it comes to tryingto handle that, handle that
(01:11):
decoherence, that's one of thebig areas that there's
challenges, that they're tryingto be able to increase that
error correction capability, beable to essentially have quantum
computers produce more accurateresults.
Because right now it's prettyprone to noise.
Any bit of radiation that isable to get through to the
(01:34):
quantum chip can actually flipthose bits randomly, so they
need to have a lot of shieldingto protect against radiation.
There's a lot of differentaspects around the actual
physical qubit itself, but alsothere's only so many qubits
available right now on quantumcomputing chips.
So to be able to solve a lot ofproblems you need a thousand
plus qubits, where right nowsome of the top quantum
(01:55):
computers, depending on whatarchitecture they are, have, you
know, 50 to maybe hundreds ofqubits, while you know quantum
annealing computers essentiallyhave a little bit more, but it's
kind of it's limited by howmany qubits are available for
each hardware and processing.
Maida Zahid (02:13):
Before we move on to a little bit more challenges,
I kind of wanted to understandsome more benefits that you know
we talked about earlier.
So going beyond just energyutilities, let's say supply
chain.
So you talked aboutoptimization earlier.
So how can we kind of dive intothe benefits from that
perspective in, let's say,supply chain logistics?
Curtis Nybo (02:33):
Yeah, so that's actually the area that I spend
most of my time is aoptimization route.
So we're usually using adealing quantum computer to be
able to optimize processes fordifferent clients and solve
different problems.
And when it comes tooptimization problems, which is
usually around logistics andsupply chain, in a lot of cases
(02:57):
that includes different areaslike fuel procurement,
electricity distribution,infrastructure maintenance,
anywhere where you're actuallyhaving to plan.
So a common one is routeoptimization.
So the traveling salesmanproblem if we have so many fuel
trucks that need to makedeliveries around the world say
we have a fleet of 300 trucksand there's potentially 10,000
(03:18):
or say 2,000 fuel depots thatneed fuel delivered to, what's
the optimal way to be able toschedule that fleet of trucks to
those those uh depots?
And you can take into accountdifferent uh different variables
as well, like weather, and sothat becomes a challenging
problem for classical computersto be able to run efficiently.
(03:41):
And that's a common optimizationproblem that quantum annealing
computers especially are reallygood for solving.
Another one's demandforecasting.
We've done quite a bit of workaround predictive analytics, so
trying to plan where certainmaterials should be at a certain
time.
It's very similar to the routeoptimization problem, but
(04:04):
overall when it comes to energyand utilities.
Electricity distribution is aninteresting one that I've
thought about, but we haven'thad a chance to implement it.
But it would be a similaroptimization problem to that
route optimization.
Maida Zahid (04:21):
And I think it definitely goes beyond energy
utilities too, but a very commonchallenge, absolutely just like
cybersecurity.
So we know cybersecurity isprobably one of the biggest
things, just along with supplychain, that we hear all sorts of
organizations talk about ordeal with.
So how can we use quantumcomputing for that, or the
benefits per se?
Curtis Nybo (04:42):
Yeah.
So cybersecurity is aninteresting one, and that's
probably where most people hearabout quantum computing, because
right now, the currentencryption method for pretty
much everything on the internetis using RSA cryptography, which
is essentially trying togenerate huge prime numbers,
multiply them quickly togetherand then to be able to crack
(05:04):
that encryption you need to beable to factor what prime
numbers were multiplied tocreate the larger number, which
is a very hard problem to solve,and right now it can be.
A good thing about it is if youthink of it as a password, that
password can be.
If computers got more powerful,say twice as more powerful
tomorrow, all we'd have to dowith classical computers to make
(05:26):
it harder is add another digitto it, make it, make that a
little bit longer and it becomesin like exponentially more
powerful, whereas quantumcomputers they're.
It's now with quantum computersthat factoring of large numbers
is is very possible using somealgorithms like shore's
algorithm, which has been aroundfor a long time.
They've never been able toreally test it until quantum
(05:48):
computers actually started tobecome feasible, and so an
algorithm like Shor's algorithmnow allows essentially for the
factoring of those large numbers.
So you can essentially take anRSA key, find out what those
keys were that were multipliedtogether to create it, and then
you've essentially destroyed alot of the RSA cryptography that
exists today, and so this posesa serious threat to the usual
(06:11):
classical cryptographic systems.
The good news is that itdoesn't exist yet.
No one's been able to.
A quantum computer, essentiallywith enough qubits to be able
to run that algorithm doesn'texist yet.
But the scary part is and Ithink what most organizations
are worried about is that datacan be stolen today that's
encrypted, and then thieves justhave to hang on to that data
(06:34):
and say, maybe five years, 10years down the road this becomes
possible to be able to crackthat RSA encryption.
Now they can get that data.
They can then unlock that datalater on.
Even though the data might beout of date, it may still have,
you know, quite a bit ofpersonal information.
People only have socialsecurity numbers, for you know
their whole lives.
So it doesn't matter when thatdata is stolen in the future.
(06:55):
That data might be able to beexploited in the future.
And so the other side of it isquantum computing does provide a
bunch of different ways tocreate quantum resistant
algorithms.
So there's things like quantumkey distribution, where we're
using quantum mechanics tocreate essentially an
unbreakable encryption.
That's quantum proof, butthere's also some kind of
(07:18):
standard classical ways tocreate quantum resistant
encryption as well, so thingslike lattice-based cryptography.
There's a few common metrics,there are methods that are being
explored today, and so I thinkin the future what we'll see is
a bit of a change and a moveaway from the current framework
that we use for encryption intoa more quantum, safe encryption
(07:39):
going forward, but I thinkthat's still quite a few years
away a few years away.
Maida Zahid (07:48):
For when you're, let's say, when you're talking
to clients and trying to putthese benefits into layman's
terms, what are some of the keypoints that you come across and
you talk to, like, let's say,business focused clients that
quantum computing can deliver?
So what are the benefits thatyou are most commonly talking
about among these industries?
Curtis Nybo (08:00):
The most common benefits we're talking about
when it comes to energy andutilities is usually that
optimization type of that arecurrently running models that
are taking in a lot ofinformation.
It's taking maybe hours or daysto run a single run of that
computation.
That's a good candidate forquantum computing, especially
for quantum annealing, and so werun into that quite often and
(08:25):
that's usually where we spend alot of our time.
It also there's also a lot ofuse cases around, like we talked
about cryptography.
But overall I think what we'reseeing is mostly around the
optimization front with quantumcomputing, and so lots of, lots
of organizations are findingjust that current computation
(08:46):
can take a long time.
Quantum can potentially speedthat up, and it's important to
note too that a good problem forquantum optimization isn't one
that has just a huge amount ofdata.
That's not necessarily whatwe're looking for.
We're looking for a lot ofproblems that have a lot of
variables.
So going back to, like ascheduling problem, a large
(09:07):
fleet of trucks and also a lotof constraints.
Those trucks have workers thatcan only work between eight to
five.
They can't run at night.
They have to use a certainspecified amount of fuel.
So we're looking for problemsthat have a large set of
variables and a large set ofconstraints, and then we can
(09:28):
basically find that minimumenergy state of that problem by
minimizing that objectivefunction, which is, you know,
all those variables andconstraints, and try and find
the most optimal solution tothat, to that result.
And so what that would looklike is we'd end up with a
somewhat of a schedule thatwould tell you know which trucks
to go where, at what time andwhat the best routes would be to
(09:50):
follow.
So it's not necessarily that wewant a lot of data, like I said
, um, lots of times, it's theproblem itself.
Uh, can needs to be able to beformulated into a quantum
problem, which is usually thatLots of variables and lots of
constraints.
Maida Zahid (10:06):
I think we covered a lot.
You know sample use cases,challenges and looking ahead.
How do you see this kind ofscaling for energy utilities, or
how do you see this coming?
Where do we stand now?
It seems like we're pretty inthe early stages.
How do you see this coming?
Where do we stand now?
Like it seems like we're prettyin the early stages.
How do you see this movingforward?
Curtis Nybo (10:23):
So, moving forward, there's still a lot of work to
do.
Like I said, I think I talkedabout a few of those challenges
before, and mainly around thingslike error correction,
increasing coherence times,basically stabilizing those
qubits for longer so we can getbetter measurements when we
measure them, which results inbetter accuracy for our solved
(10:43):
problems.
But overall I think, lookinginto the future, we'll see
advancements in the quantumhardware.
So I talked a little bit abouthow a lot of problems today,
like the cybersecurity problemwith Shor's algorithm, can't
really be used today becausethere's just not enough qubits
within the quantum hardware tobe able to solve that problem.
And I think we'll start to seeadvancements in quantum hardware
(11:05):
.
So Google released their newquantum computer.
We're starting to see moreplayers into the game.
We're seeing IonQ, regetti.
They're big quantum providersand overall they're racing to
try and see who can basicallyachieve quantum supremacy the
fastest.
So which computers, whichcomputer architecture can solve
(11:27):
problems faster than a classicalcomputer is what everybody's
going for, and so we're startingto see a lot more advancements
and I think those advancementswill start to really pick up.
Another area that we're seeingexpand is the access to quantum
computing.
So for those wondering how toget started with quantum
computing.
It seems like it might be farout there, but it's actually not
.
Quantum computing is availablethrough most cloud platforms.
(11:48):
So Azure, quantum services, aws, bracket, even a lot of the
quantum providers, like D-Wave,have their own cloud provider
cloud platforms and so you canget in and actually utilize
these quantum computers to helpsolve those problems or even
just to experiment.
You can start using that today,so the barriers to entry, I
think, will also drop quite abit.
(12:10):
Hopefully.
It gave a bit of an overview ofquantum computing and some of
the capabilities.
My advice would be to kind ofjust dive right in.
Like I said, a lot ofcapabilities are available.
A lot of the places we spend alot of our time is just
experimenting with, trying toshape different problems into
quantum capable problems andthen we try and solve them using
(12:33):
quantum computers.
So the technology is here,despite what you might be
hearing, and I said, anybody canaccess it today.
Thanks so much, curtis.
Maida Zahid (12:39):
Thanks might be hearing and like I said, anybody
can access it today.
Thanks so much, curtis.
Thanks for joining us andthanks for listening everybody.
Please subscribe to our podcaston Apple Podcasts and Spotify
or wherever you get yourpodcasts from.
Thanks for joining us.
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