# Quantum Machine Learning Explained

TLDRThis video delves into the intersection of quantum computing and machine learning, highlighting the potential for quantum computers to tackle complex classification problems by leveraging higher-dimensional feature spaces. It explains how classical machine learning struggles with non-linear data, while quantum computing, through quantum circuits and kernel functions, can provide exponential speedups. The video also mentions IBM's Qiskit Runtime and its sampler primitive, which can be used to build quantum machine learning algorithms, and points viewers to further resources for learning.

### Takeaways

- 🌟 Quantum computing is being explored for applications in machine learning, an exciting field that intrigues many developers.
- 📊 Classical machine learning often involves linear classification problems, which can become complex with non-linear data sets.
- 🔍 To handle complex data, classical methods map data into a higher dimensional feature space using kernel functions.
- 🚀 Quantum computers can access more complex and higher dimensional feature spaces, potentially offering advantages over classical computers.
- 💡 Quantum computers can encode data into quantum circuits, which may result in kernel functions that are intractable for classical systems.
- 📈 IBM researchers demonstrated in 2021 that quantum kernels can provide an exponential speedup for certain classification problems.
- 🔬 There is ongoing research to improve quantum kernels, especially with structured data and kernel alignment.
- 🛠️ Qiskit Runtime offers tools like the sampler primitive, which can be used to build quantum machine learning algorithms.
- 🔄 The sampler primitive helps in obtaining quasi-probabilities that reveal relationships between data points, forming a kernel matrix for classification.
- 📚 For those interested in quantum machine learning, resources like Qiskit Runtime and courses in the Qiskit textbook are available for further learning.

### Q & A

### What is the main topic discussed in the video?

-The main topic discussed in the video is the application of quantum computing in machine learning, specifically focusing on quantum machine learning and its potential advantages over classical machine learning.

### Why are classical machine learning developers excited about quantum computing?

-Classical machine learning developers are excited about quantum computing because it offers the potential to solve complex problems more efficiently, especially in areas where classical methods face limitations due to data complexity and computational power.

### What is a common problem in classical machine learning that the video discusses?

-The video discusses the problem of linear classification in classical machine learning, where data points need to be separated into two discrete groups, which can be challenging if the data is not linearly separable.

### How does mapping data into a higher dimensional space help in classification problems?

-Mapping data into a higher dimensional space, or feature space, can make it easier to classify complex data sets by allowing for the possibility of finding a hyperplane that separates the data points into distinct categories.

### What are kernel functions in the context of machine learning?

-Kernel functions in machine learning are used to map data into a high-dimensional feature space without explicitly computing the coordinates of the data in that space. They are powerful tools for transforming data to make it more amenable to classification or regression.

### What challenges do kernel functions face in classical machine learning?

-Kernel functions in classical machine learning can face challenges such as poor results with certain data sets, and computational runtimes that can explode as the complexity of the data sets increases.

### How do quantum computers provide an advantage in machine learning?

-Quantum computers can provide an advantage in machine learning by accessing more complex and higher dimensional feature spaces than classical computers. They can encode data into quantum circuits, potentially resulting in kernel functions that are difficult or impossible to replicate classically.

### What did IBM researchers prove about quantum kernels in 2021?

-In 2021, IBM researchers proved that quantum kernels can provide an exponential speedup over their classical counterparts for certain classes of classification problems.

### What is Qiskit Runtime and how does it relate to quantum machine learning?

-Qiskit Runtime is a set of tools that allows for the easy building of quantum machine learning algorithms. It includes features like the sampler primitive, which are unique to IBM's Qiskit Runtime and help optimize workflows and execute them efficiently on quantum systems.

### How can the sampler primitive be used in a quantum machine learning context?

-The sampler primitive in Qiskit Runtime can be used to obtain quasi-probabilities that indicate relationships between different data points. These relationships can be used to construct a kernel matrix, which can then be evaluated and used in a classical support vector machine to predict new classification labels.

### Where can one find more information about Qiskit Runtime and quantum machine learning courses?

-More information about Qiskit Runtime and quantum machine learning courses can be found in the links provided in the video description.

### Outlines

### 💡 Quantum Computing and Machine Learning

The speaker introduces the exciting intersection of quantum computing and machine learning, highlighting the potential for quantum computing to revolutionize machine learning applications. They begin with a discussion of a common classical machine learning problem: linear classification. Using simple visual examples, they illustrate how data can be easily classified when it lies on a single linear plane but becomes complex when the data points are intermixed. The solution to this complexity is to map the data into a higher-dimensional feature space using kernel functions. However, kernel functions can be computationally expensive and may not always yield optimal results. Quantum computers, with their ability to access higher-dimensional feature spaces, offer a promising alternative. The speaker mentions that quantum computers can encode data into quantum circuits, potentially creating kernel functions that are more powerful than classical ones. They reference a 2021 IBM study that demonstrated quantum kernels could provide an exponential speedup for certain classification problems. The paragraph concludes with a mention of ongoing research in quantum kernel development and the use of Qiskit Runtime for building quantum machine learning algorithms.

### 🔍 Quantum Machine Learning in Practice

In the second paragraph, the speaker delves into the practical application of quantum machine learning, specifically using the Qiskit Runtime and its sampler primitive. They explain how, once data is encoded into a quantum circuit, the sampler primitive can be used to obtain quasi-probabilities that reveal relationships between data points. These relationships form a kernel matrix, which is crucial for machine learning tasks. The kernel matrix can then be employed in a classical support vector machine to predict new classification labels. The speaker encourages viewers to explore quantum machine learning further, directing them to resources such as the Qiskit textbook and related courses. The paragraph ends with a call to action for the audience to engage with the material and a thank you for watching.

### Mindmap

### Keywords

### 💡Quantum computing applications

### 💡Machine Learning

### 💡Linear classification

### 💡Feature space

### 💡Kernel functions

### 💡Quantum circuits

### 💡Exponential speed up

### 💡Quantum kernel

### 💡Qiskit Runtime

### 💡Sampler primitive

### 💡Support Vector Machine (SVM)

### Highlights

Quantum computing applications in machine learning are a very exciting area of research.

Classical machine learning developers are interested in quantum computing's potential applications.

Linear classification is a common classical machine learning problem.

Data can be classified into two discrete groups using a single line if arranged linearly.

Complex data arrangements require mapping into a higher dimensional feature space for classification.

Kernel functions map data into high dimensional feature spaces for classification.

Kernel functions can face issues with poor results and increased compute runtime.

Quantum computers can access higher dimensional feature spaces than classical computers.

Quantum circuits can encode data for complex kernel functions.

IBM researchers proved quantum kernels can provide an exponential speedup for certain classification problems.

Research is ongoing to improve quantum kernels with structured data and kernel alignment.

Qiskit Runtime offers tools to build quantum machine learning algorithms.

Sampler primitive is a tool in Qiskit Runtime for optimizing workflows on quantum systems.

Quasi-probabilities from quantum circuits can constitute a kernel matrix for machine learning.

The kernel matrix can be used in a classical support vector machine for classification.

Qiskit textbook offers a course on quantum machine learning.