Today, I'm going to talk to you about quantum computing applications and
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 this 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 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 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 transferring our data,
mapping it into a high dimensional feature space? To do this, we can use kernel functions.
Kernel functions work by taking some underlying features of the original dataset 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 get poor
results, and also the compute runtime can explode as the complexity of the data sets increase.
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 provide an advantage in this space.
They can be useful because quantum computers can access much 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.
In 2021, IBM researchers actually proved that quantum kernels can provide an exponential
speed up over their classical counterparts for certain classes of classification problems.
As well as this. There is a lot of research going into improving Quantum kernel with structure,
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. And you can use Qiskit Runtime
to easily build a quantum machine learning algorithms with built in tools such as the
sampler primitive, which primitives are unique to the IBM's Qiskit 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 Kiska
runtime as well as the quantum machine learning course that's available on the Qiskit textbook.
I hope you've enjoyed this content. Thank you very much for watching. [00:00:00][0.0]