Quantum Kernels Accelerate ML Classification
Key Points
- The speaker explains that linear classification often requires mapping data into a higher‑dimensional feature space using kernel functions to make the classes linearly separable.
- Classical kernel methods can become computationally expensive or give poor results when dealing with highly correlated, complex, or high‑frequency time‑series data.
- Quantum computers can encode data into quantum circuits, enabling the construction of quantum kernel functions that explore vastly richer feature spaces than classical kernels can.
- IBM researchers demonstrated in 2021 that quantum kernels can achieve exponential speed‑ups over classical kernels for certain classification tasks.
- Current research is focused on improving quantum kernels for structured data and expanding their practical applications in machine learning.
Full Transcript
# Quantum Kernels Accelerate ML Classification **Source:** [https://www.youtube.com/watch?v=NqHKr9CGWJ0](https://www.youtube.com/watch?v=NqHKr9CGWJ0) **Duration:** 00:05:56 ## Summary - The speaker explains that linear classification often requires mapping data into a higher‑dimensional feature space using kernel functions to make the classes linearly separable. - Classical kernel methods can become computationally expensive or give poor results when dealing with highly correlated, complex, or high‑frequency time‑series data. - Quantum computers can encode data into quantum circuits, enabling the construction of quantum kernel functions that explore vastly richer feature spaces than classical kernels can. - IBM researchers demonstrated in 2021 that quantum kernels can achieve exponential speed‑ups over classical kernels for certain classification tasks. - Current research is focused on improving quantum kernels for structured data and expanding their practical applications in machine learning. ## Sections - [00:00:00](https://www.youtube.com/watch?v=NqHKr9CGWJ0&t=0s) **Quantum Feature Mapping for Classification** - The speaker introduces quantum computing’s relevance to machine learning by illustrating how projecting data into a higher‑dimensional feature space can transform a hard linear‑classification problem into an easily separable one. ## Full Transcript
today I'm going to talk to you about
Quantum Computing applications in
machine learning this is a very exciting
area of quantum Computing research and
lots of classical machine learning
developers are understandably excited
about the potential applications within
their own field
so to get started let's talk about a
classical machine learning problem that
is one that's very common linear
classification
so if we start with two sets of data
that we want to classify into two
separate categories let's draw them here
we're just going to have
Three Dots and three crosses all on a
single linear plane here if we arrange
if the data is arranged like this it can
be pretty easy to classify this into two
discrete groups we can draw a single
line in the middle here and now we've
classified them
but this can be a lot harder if our data
is more complex
for example if our data is arranged like
this
perhaps with the crosses in the middle
now there isn't a single line that we
can draw
um on this plane to classify the data
into two discrete groups
so in order to solve this problem and
classify this data what we need to do is
we need to map this data into a higher
dimensional space which we're going to
call a feature space
then if we've mapped the data for
example like this
we can now see because we've mapped this
data into a high dimensional space there
is now a much easier way to classify
this
so how do we do this step of uh
transferring our data mapping it into a
higher dimensional feature space to do
this we can use kernel functions
kernel functions work by taking some
underlying features of the original data
set and using that to map those data
points into this High dimensional
feature space
kernel functions are incredibly powerful
and Incredibly versatile but they do
face problems sometimes they just give
poor results
um and also the compute runtime can
explode as the complexity of the data
sets increase
if you're a if you're an experienced
machine learning developer perhaps
you've seen this already if you're
dealing with data that has very strong
correlations or perhaps if you're
dealing with time series forecasting
where the data is very complex and at a
high frequency
but quantum computers have the potential
to
um provide an advantage in this space
they can be useful because quantum
computers
can access much uh more complex and
higher dimensional feature spaces than
their classical counterparts can
and they can do this because
quantum computers can we can encode our
data into Quantum circuits and the
resulting kernel functions could be very
difficult or even impossible to
replicate on a classical machine as well
as this those kind of functions also can
perform better
uh in 2021
IBM researchers
um actually proved that Quantum kernels
can provide an exponential speed up over
their classical counterparts for certain
uh classes of classification problems
um as well as this there is a lot of
research going into improving Quantum
kernels with structured data and kernel
alignment so as you can see this field
is incredibly exciting there's a lot of
research going on in this space
um and you can use kiss kit runtime
to easily build a Quantum machine
learning algorithms with built-in tools
such as the sampler primitive which
Primitives are unique to uh the IBM's
kisket runtime these are essentially
predefined programs that help us to
optimize workflows and execute them
efficiently on Quantum systems
let's take for example our linear
classification problem let's say we have
our data and we've encoded it into a
Quantum circuit
we can then use the sampler primitive
to obtain quasi-probabilities indicating
the relationships between the the
different data points and these
relationships can constitute our kernel
Matrix
and that kernel Matrix can then be
evaluated and used in even a classical
support Vector machine
to predict new classification labels
so if you're ready to get started
learning more about Quantum machine
learning you can check out the links in
the description for more information
about kisket runtime as well as a
Quantum machine learning course that's
available on the kiskit textbook I hope
you've enjoyed this content thank you
very much for watching