https://www.youtube.com/watch?v=NqHKr9CGWJ0
Quantum Machine Learning Explained
Quantum Computing Essentials
IBM Quantum Computing Roadmap → https://ibm.biz/BdPzaR
Quantum computers have the potential to solve certain classes of problems exponentially faster than any known classical techniques. In most cases, the theoretical proofs behind these speedups are decades old, but one exception to that rule is the exciting and highly active field of quantum machine learning (QML). In 2021, IBM researchers proved that quantum kernels can provide an exponential speedup over classical counterparts for certain classification problems. In this video, IBM Quantum developer advocate Abby Mitchell shows how QML methods give classical ML a boost, and explains how developers can start building their very own QML algorithms with Qiskit Runtime.
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#software #ITModernization #QuantumComputing #QuantumMachineLearning #QML #MachineLearning #ML
– Introduction to Quantum Machine Learning: Discusses the intersection of quantum computing and machine learning, highlighting its potential applications and appeal to classical machine learning developers.
– Classical Machine Learning Example: Explains the challenge of linear classification in traditional machine learning with simple and complex data arrangements.
– Kernel Functions: Describes how kernel functions map data into higher dimensional feature spaces to aid in classification, noting their power but also their limitations in handling complex data.
– Advantages of Quantum Computers: Quantum computers are noted for their ability to access more complex and higher dimensional feature spaces than classical computers, potentially offering exponential speed-ups for certain classification problems.
– IBM’s Qiskit Runtime: Introduces Qiskit Runtime and its tools like the sampler primitive, which helps build and optimize quantum machine learning algorithms, making them accessible and efficient for developers.
“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”