QC101 Quantum Computing & Intro to Quantum Machine Learning
Math-Based Introduction to Quantum Computing, Cryptography & Quantum Machine Learning. Code with Python, Q#, & Qiskit
4.53 (3429 reviews)
19 582
students
12 hours
content
Dec 2024
last update
$84.99
regular price
What you will learn
Use quantum cryptography to communicate securely
Develop, simulate, and debug quantum programs on IBM Qiskit and Microsoft Q#
Run quantum programs on a real quantum computer through IBM Quantum Experience
Use Dirac's notation and quantum physics models to analyze quantum circuits
Train a Quantum Support Vector Machine (Quantum Machine Learning) on real-world data and use it to make predictions
Learn Data science and how quantum computing can help in artificial intelligence / machine learning
Learn why machine learning will be the killer-app for quantum computing
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Our Verdict
*QC101 Quantum Computing & Intro to Quantum Machine Learning* offers an expansive look into the world of quantum computing, providing learners with a math-based and accessible approach. Despite minor areas for improvement such as providing more practice opportunities and expanding on machine learning topics, the course delivers value through clear explanations of complex topics backed by hands-on coding tasks using Q# and Qiskit. Ideal candidates include STEM professionals looking to dive into quantum computing or expand their knowledge in artificial intelligence & machine learning using a quantum perspective.
What We Liked
- Comprehensive coverage of quantum computing concepts, including cryptography and machine learning
- Instructor explains complex topics in an easy-to-understand manner using analogies and clear examples
- Well-designed course structure with a strong focus on fundamentals makes it accessible for learners at various levels
- Hands-on coding experience using popular quantum programming languages such as Q# and Qiskit
Potential Drawbacks
- Occasional repetition of basic concepts, which might be unnecessary for more experienced learners
- Machine learning portion could be more in-depth, specifically on converting classical to quantum algorithms
- Lack of practice problems and guided exercises for better hands-on experience
- Instructor sometimes assumes certain questions without explicitly addressing them
1861396
udemy ID
17/08/2018
course created date
26/02/2020
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