62 AI BASICS Quantum Machine Learning Part 2

Sinsavk AI for beginners · Beginner ·📐 ML Fundamentals ·4mo ago

About this lesson

Link to my YT channel SINSAVK AI FOR BEGINNERS https://www.youtube.com/channel/UCWYy-VfH3A92kS4HNWZXsMA Quantum Machine Learning is an emerging field at the intersection of quantum computing and artificial intelligence, aiming to use quantum systems to enhance how machines learn from data. While classical machine learning relies on traditional computers that process bits as zeros and ones, quantum computing introduces a fundamentally different model based on qubits, which can exist in multiple states simultaneously due to the principle of superposition. This property allows quantum systems to represent and process a vast amount of information in parallel. In theory, this could enable certain machine learning tasks to be performed much faster than on classical hardware. Another key concept is entanglement, where qubits become correlated in such a way that the state of one instantly influences another, regardless of distance. These quantum phenomena create new possibilities for encoding, transforming, and analyzing data. In classical machine learning, data is typically represented as vectors in high-dimensional space, and models learn patterns by adjusting parameters through optimization techniques. In quantum machine learning, data can be encoded into quantum states, and transformations are performed using quantum gates. These gates manipulate qubits in ways that can represent complex mathematical operations more efficiently than classical equivalents in specific scenarios. One of the most discussed approaches in this field is the use of quantum circuits as models. These circuits consist of sequences of quantum gates that process input data and produce outputs that can be measured and interpreted. By adjusting the parameters of these gates, similar to tuning weights in a neural network, the system can be trained to perform tasks such as classification or regression. This approach is often referred to as variational quantum algorithms. A critical step in quantum mach

Original Description

Link to my YT channel SINSAVK AI FOR BEGINNERS https://www.youtube.com/channel/UCWYy-VfH3A92kS4HNWZXsMA Quantum Machine Learning is an emerging field at the intersection of quantum computing and artificial intelligence, aiming to use quantum systems to enhance how machines learn from data. While classical machine learning relies on traditional computers that process bits as zeros and ones, quantum computing introduces a fundamentally different model based on qubits, which can exist in multiple states simultaneously due to the principle of superposition. This property allows quantum systems to represent and process a vast amount of information in parallel. In theory, this could enable certain machine learning tasks to be performed much faster than on classical hardware. Another key concept is entanglement, where qubits become correlated in such a way that the state of one instantly influences another, regardless of distance. These quantum phenomena create new possibilities for encoding, transforming, and analyzing data. In classical machine learning, data is typically represented as vectors in high-dimensional space, and models learn patterns by adjusting parameters through optimization techniques. In quantum machine learning, data can be encoded into quantum states, and transformations are performed using quantum gates. These gates manipulate qubits in ways that can represent complex mathematical operations more efficiently than classical equivalents in specific scenarios. One of the most discussed approaches in this field is the use of quantum circuits as models. These circuits consist of sequences of quantum gates that process input data and produce outputs that can be measured and interpreted. By adjusting the parameters of these gates, similar to tuning weights in a neural network, the system can be trained to perform tasks such as classification or regression. This approach is often referred to as variational quantum algorithms. A critical step in quantum mach
Watch on YouTube ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
Modal — Deep Dive
Learn how Modal Labs provides a high-performance, Python-native compute platform for data scientists, ML engineers, and AI researchers, and how it can help eliminate infrastructure tax
Dev.to AI
 📰
Attention, Explained the Way I Wish Someone Had Explained It to Me
Learn how attention works in transformers and its importance in deep learning models
Medium · Data Science
 📰
Valid Parenthesis
Learn to validate parentheses in Python using a stack data structure and implement a solution to check for valid parentheses in a given string
Medium · Python
 📰
Feature Store Refactoring: Build Once, Reuse Everywhere
Learn how to refactor a feature store to reduce deployment times and infrastructure costs by consolidating feature logic into a single dbt backbone
Medium · Machine Learning
Up next
AI & Machine Learning Course Review by Tandeep Sandhu, Solutions Directior
Great Learning
Watch →