Hello, and welcome to the Physics World weekly
podcast. I'm Hamish Johnston.
This episode
explores how quantum computing
and artificial intelligence
can be combined
to help physicists
search for rare interactions
in data from an upgraded
Large Hadron Collider.
But first, I'd like to thank Delft Circuits
for their generous support of this episode.
This episode is supported by Delft circuits,
a key enabler in the evolution
of quantum technologies.
While annealing quantum computers are already demonstrating
practical value,
Gate based systems are still on their way
to large scale implementations.
One of the biggest challenges
is IO,
reliably connecting thousands of qubits
without compromising
performance.
Delft circuits
addresses this bottleneck with a fundamentally
different approach to cryogenic
connectivity.
Their flexible,
multichannel
planar cabling
integrates filtering components and delivers
exceptionally
low heat load,
paving the way to kilo cubit architectures
because advancing science
requires
infrastructure
built for the quantum age.
In mid twenty twenty six, the Large Hadron
Collider at CERN will shut down and undergo
the final stage of the high luminosity
upgrade.
The aim is to increase the number of
particle collisions
in the LHC
by about a factor of 10.
By 2030,
this should allow physicists
to study rare particle
interactions
that are not visible today.
These interactions
create showers of particles
that are detected
by huge LHC
experiments
including
ATLAS and CMS.
These showers create distinctive
patterns
in the detector data
which provide details
of the sought after particle interactions.
The challenge for physicists
is to isolate the desired signals
within the plethora of data that will be
produced by an updated
LHC.
To do this, they must know how showers
from rare interactions
will interact
with the detectors,
and this is done using
computationally
intensive
simulations.
Therefore,
it's not surprising that particle physicists
are looking to quantum computers
and artificial intelligence
to boost the performance
of these simulations.
Recently,
I was at Canada's
Perimeter Institute
and spoke to Javier
Toledo
Marine
about the use of quantum assisted
generative models
in particle physics.
Javier is based at the Triumph
Particle Accelerator
Center in British Columbia,
and he also has an appointment at Perimeter.
Here's that conversation.
So I'm at the Perimeter Institute in Waterloo,
Ontario, and I'm joined by Javier Toledo
Marine.
Hi, Javier. Welcome to the podcast.
Hey, Hamish. Thank you.
So Javier, you've recently published a paper that
describes how quantum assisted artificial intelligence
can be used to simulate how high energy
particles
interact with detectors
such as ATLAS and CMS
on the Large Hadron Collider.
Why do physicists want to do these calculations,
and why can't they be done using conventional
computers?
Yeah. So that's a great question. That's, I
mean, that's the starting point.
So
as you might know,
the Large Hadron Collider is closing. It's shutting
down,
for a few for some time now,
because they're updating upgrading some of their detectors.
So, so the new run of the LHC
is called the high luminosity LHC.
And so it's called high luminosity
because, fundamentally, they're increasing the collision rate.
So it's being increased by tenfold, I believe.
And so that means that when they do
these runs, we'll be getting more data. Right?
And so this data is important because if
you're trying to
look at rare events,
like, for instance, the dye Higgs, self interaction
prompt,
well, you actually have to it turns out
that the dye Higgs is 1,000
times less often than just the Higgs.
So you need a lot more data, and
so that's why it's being upgraded. And on
the simulation side,
you also need
similar amount of data,
and so that is becoming increasingly
challenging.
And one of the reasons why it's challenging
is because of the
calorimeter
pipeline in the simulation.
So there's in the ATLAS
detect the ATLAS experiment, there's a detector, which
is the calorimeter.
And so when particles go through this calorimeter,
they deposit the energy,
but they also create secondary particles. And so
they create these these showers.
And so when that happens in the simulation,
you need to track all of these particles
as they go through the detector.
And so you can imagine that if you
increase the number of particles going through that,
then you're going
to need be needing more data from that.
And so that is where it's becoming challenging
because with the current
computational capabilities, it turns out that it's
unsurmountable
at this point, using the traditional methods.
So that is why people around the world,
scientists, they're looking to use,
different tools like deep generative models
to generate these showers.
So in our case, we're combining deep generative
models with,
quantum computing or, in this case, quantum annealers.
So I think that answers the question. Right.
So so the idea is that you're you're
gonna have so many collisions
going on in these detectors
that,
and you need to or I I suppose
the physicists at CERN will need to focus
in on a specific,
event. But, of course, they they probably don't
know exactly what that event will look like,
and that's where your research comes in. You
you're you do your simulations and you say,
well, we think
the event you're looking for
is going to look like this in the
detector. Yeah. That that's correct. So so basically,
like, you have your experiment and the way
to validate your experiment with theory, you do
these simulations and then you compare your hypothesis
from your simulations with what you actually get
from from this experiment. Correct? I see. Okay.
And can you explain in relatively simple terms,
I mean, that might not be
that might not be an easy task,
How,
your quantum assisted
generative
model works?
Sure. Yeah. That's, okay. So let me give
it a try.
So, I mean, let let me start with
generative models. So generative models, they're,
way older than the current revolution that we're
living with, with AI.
And you can think about it as the
like, they come to solve the task of
generating
data, generating samples from specific probability distributions.
Right? So sometimes, like, many years ago when,
you didn't have all these, programming languages, like,
when you wanted to generate, Gaussian distributed random
numbers,
you would normally use,
the Box Mueller method in which you would
generate uniformly distributed random numbers, and then you
would give those random numbers to a function,
and then you would get your Gaussian distributed
random numbers.
These days, you you I mean, you just
use your whatever. If you're using Python, it
already has a a package that does that
for you. So in that way, it's like
we're trying to generate what we're working on
is generating these showers, which come from a
probability distribution,
but we don't know that probability distribution a
priori
rate. So what we're doing is that we're
sampling from an easier distribution,
and then we pass it through some function.
In this case, this function is a neural
network, and then it gets converted to the
shower.
Now the way we are doing it is
that we use quantum annealers as the initial
sampler.
So we sample from the quantum annealer. We
get a a random vector.
We pass it through a neural network, and
we get the shower.
Now that's the way once the model is
trained, we we use it. Right? To train
it, it's a bit more complicated.
But, essentially,
it's
you can think about it as a data
driven machine learning problem in which you have
a data set that you feed to this
model and you try to reconstruct it.
And by reconstructing it, then you're you're, fine
tuning these parameters in your neural network.
And then you use this sampler, the quantum
analyzer, to to sample from it and pass
it through the through the neural
network. I see. And and the data that
you're using, is that
data from
scattering experiments that have been done
in detectors?
Or
do you also incorporate
physics
into that as well, or is it purely
just the the data, or is it both,
I suppose?
So we started off by using this dataset
called the Cayo Challenge.
And so the Cayo Challenge is a challenge
that started, it was launched in 2022,
by a group of,
scientists around the world. And so the the
scope of this challenge is precisely to see
if we can use generative models to,
to to sample, to generate these showers.
So it's a publicly available dataset,
and it's a
different dataset than what you would normally get
from the simulation
pipeline that they used in in ATLAS. Right?
It's still generated from that simulation pipeline, but
it's then processed in a way that you
might say it makes it maybe easier for
the neural network to learn it. Right? That
I mean, very yeah.
So that's the dataset that we used in
this this paper.
And so right now, we're now looking to
use
data that's actually generated for the ATLAS experiment,
more realistic dataset. Right? Now this dataset is
composed by like, you think think about it
this way. So when the collision happens, you
get,
particles going through that. Right? And then these
particles go through the detectors. Right? So the
data that we're feeding through to to our
model, it's the data in which you you
have that an incident particle entering the calorimeter
with a specific energy
and a specific type. Right? So a specific
energy in the order of of GeV,
giga electron volts. And the type of particle
that we've considered thus far is, electrons. Right?
But we are also looking to consider,
photons and pions in the near future.
I see.
So Javier, you've used a a quantum annealer
from D Wave systems.
What is quantum annealing? And how does it
differ from the the gate based models of
quantum computation that
maybe some of our listeners will be more
familiar with.
Right. Yeah. So then
okay. So you have
basically two different types of quantum computing, which
is, like you mentioned, gate based
and adiabatic quantum computing.
So in theory, both of them are universal,
quantum tuning in the sense that you can
do any quantum algorithm and and using both
technologies.
In practice, there are nuances. You have, like,
decoherence. You have noise in it in both
cases.
And so in the case of gate based
quantum computing,
what you're using there are unitary gates that
operate on the qubits.
So you can think about it more like
a in a digital way.
Whereas adiabatic quantum computing, you're
continuously
evolving your quantum system with, what's called an
evolution operator.
You're you're evolving your system.
And what this adiabatic quantum computing does, it
finds the ground state of a specific Hamiltonian
that encodes your problem.
Now like I said, in practice, there are
lots of nuances
related
to decoherence
and noise and error correction and and error
in your in your system.
So in our case, we're not when we
evolve this simultaneous to get the the ground
state, we're not really interested in the ground
state.
We're interested in getting a
sample that comes from a Boltzmann distribution.
So it just happens that
this quantum annealer,
when you,
when you do the annealing process
in a specific time window, you're going to
get a sample that comes from a Boltzmann
distribution. Right? And so we use that because,
basically, our framework is a combination of what's
called a variational autoencoder
and what's called a restricted Boltzmann machine.
So this restricted Boltzmann machine is,
like I mentioned earlier, where we sample from.
We use it as a sampler.
And and so that makes it,
oh, like, basically a direct way to sample
from a quantum annealer. Like, it's completely straightforward,
sampling from a quantum annealer,
and using it as a as a quantum
version of a restricted Bose machine. I see.
And so so is the is the idea
that, you know, you've you've this electron has
gone into the detector,
and it's going to interact.
And there's lots of different
things that can happen.
You know, this interaction can occur, then that
interaction can occur. And, you know, it probably
just blows up into
a huge,
I suppose, space of possible things.
Do is the idea of the of the
quantum
annealer, does it give you the most probable,
outcome
of of that collision
process? Is is that what you're looking for,
or is it is it a bit more
complicated than that?
Well, I would say that it's not necessarily
the most probable,
event that we're looking for. We're just looking
to be able to sample from
from the manifold where these like, when when
you mentioned this, you can think about, like,
when this electron goes through this detector and
generates the shower
so that event,
falls in some kind of manifold.
And so we want to be able to
sample from from those manifold from from that
manifold. Right?
And so this, this quantum annealer, this this
framework of combining the quantum annealer with the
variational encoder
allows us to efficiently sample
from that, from that manifold. Right? And so
when I say efficiently, I'm thinking of mainly
two things. One is
accurately capturing
the the physics or the properties of these
these showers,
for which we we we have metrics for
that, and we can compare our
framework with other frameworks, and we're doing well
on that front. But, also, you wanna do
it fast. Right? Because in the end, that's
the the problem that we're tackling that. Can
we generate those showers,
and can we do it faster than, like,
current,
traditional methods are able to do so? Right.
And the the I mean, these the the
way that a particle will interact with a
detector, that's, I mean, that's
governed by quantum mechanics.
You know, it's all probabilities, etcetera.
Does the fact that you're using a quantum
annealer,
does that help you go faster in the
sense that, you know, the system you're trying
to simulate is quantum,
your hardware is quantum?
Therefore, you know, that that I suppose the
old argument of Richard Feynman
about, you know, why quantum computers could be
useful for studying quantum systems. Does that apply
here?
Yeah. That's a very interesting question.
I would say that at this point, it
doesn't apply because we still like, when we
sample from the quantum annealer,
we're,
I mean, we're doing a classical measurement, and
then we're passing that through a classical algorithm.
Right? So
I would say that it doesn't apply here
at this point, but that's certainly,
like, the the scope of this project. Right?
Because at this point, the the training that
we do,
it's basically
a classical training.
But we're certainly, like, we're looking forward to,
getting more from this these quantum annealers
and and using them for for this type
of physics problem. Yeah. I see. And what
does success
mean? How how have you shown that your
technique is useful for predicting
how particles interact with the detectors?
So at this point, what we're
what we're using are it's a set of
metrics that come from the kalo challenge and
also metrics that are typically used by,
high energy physicists.
And so what we're basically doing is that
we're comparing the
the performance
of our framework with traditional with the traditional
method and also with other generative models. Right?
So that's,
how success looks like in at at this
point. Right?
By looking,
how do we perform compared to to other
methods? Right? And so at this at this
stage,
we're doing
quite well in terms of quality and in
terms of
speed up. It's,
as far as we we know, it's the
the fastest method,
compared to other What what what I mean,
you mentioned speed up. What I mean, how
much faster is your method than
a method using a conventional
supercomputer
or what what whatever these calculations are done
on at the moment?
Right. Okay. So let me be nuanced here
in this this question. So we've used the
for the for the Caleb challenge, which is
the dataset that we used for this,
paper.
And our results
show that we're about
1,000 times faster than the traditional method, which
is called g e on four.
Compared to
other generative models that were part of the
Cayla Challenge,
we're also
faster than than Dose Method, currently.
I see. And are you are you at
the point where you're sort of moving out
from the research phase, let's say, to, you
know, sort of letting this thing roll and
doing calculations that you will then pass on
to physicists working at CERN?
Or is there more work to do before
you can do that?
Yeah.
Great question. So that there's still work to
be done because, like I mentioned,
we've,
we first used the Kalot challenge dataset, and
now we're moving to ATLAS generated dataset.
So now, like, we what we have to
do is
everything we showed for the kilo challenge, we
now need to be
we now need to show it for this
ATLAS dataset.
So that means we need
to train models
on multiple datasets,
run these benchmarks, and show that we're actually
as good as we should for the kill
challenge. So I think that's still gonna take,
a reason about a reasonable amount of time,
but we're certainly
looking forward to
to pushing this to to a place in
which it could be deployed. Right? Deployable, at
least.
And final question, Javier.
Are there any sort of non
particle physics
applications to this technique?
I mean, it could could it be used
in other parts of physics or you useful
to physicists working in condensed matter or, I
don't know, even biologists,
chemists,
or I mean even, I don't know, a
trucking company that wants to, you know, plan
the most efficient route across,
across Canada. Are there are there up other
applications for this, or is it only
sort of a particle physics thing?
Right. Okay. So the the what we call
the Cayo QVAE framework, which is the VAE
with the using the the quantum annealer as
a sampler,
that you that you can use it for
any
machine learning problem generative model. Right?
So you can use it either to generate
pictures
of people or of dogs or whatnot. Right?
So it's not limited to to just physics.
Right?
However, you did mention, right, can you use
it to optimize the routes of,
no, like, delivery stuff like that. Right? So
I guess that's a a good question. Right?
So so quantum annealers, the
the motivation is to to find this ground
state. Right? So there are some problems
that in principle you can map, like problems
of optimization of routes that you can map
to a quantum annealer. Right? So in that
case,
you wouldn't actually need the variational encoder. You
would just use the the quantum annealer to
to find that that, ground state of that
optimum
solution.
That's the, I suppose, is that the famous
traveling salesman
problem, which I think is really difficult, isn't
it? That's correct.
Yeah.
Yeah. That's correct. That's, that's the the salesperson,
traveling problem.
Of course,
there you might have some extra constraints, like,
in a real problem. Right? And that's where
it gets
really complicated. How how do you embed those
constraints
into the quantum in either? Mhmm. Right?
Well, thanks so much, Javier. Thanks for speaking
to Physics World about your research, and we
look forward to, to hearing more from you
and your colleagues.
Thank you so much.
That was Javier Toledo Marine
in conversation with me at Canada's Perimeter Institute.
The paper we spoke about is called Conditioned
quantum assisted
deep generative
surrogate
for particle
calorimeter
interactions,
and it's published in NPJ
Quantum Information.
I'll put a link to the paper in
the podcast notes.
I'd like to thank Delft Circuits for their
generous support of this episode,
and also thanks to Javier
Toledo
Marine
for joining me today.
And as always, our producer,
Fred Ailes.
I'll be back again next week when I'm
joined by a physicist and a sculptor
to talk about the science and art of
quantum steampunk.
See you then.
As gate based quantum computing continues to scale,
Delft circuits provides the IO solutions
that make it possible.