Quantum Computing &; Quantum Machine Learning with Python

[rlscorner]

Quantum Computing & Quantum Machine Learning with Python
Published 10/2026
Created by Sahinur Islam
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English + subtitle | Duration: 126 Lectures ( 24h 44m ) | Size: 9.2 GB

Master qubits, gates, Qiskit, Grover and Shor, then build quantum machine learning models with PennyLane and Python

What you'll learn
⚡ Explain qubits, superposition, entanglement, interference and measurement clearly and correctly
⚡ Use the linear algebra of quantum computing: complex numbers, Dirac notation, unitary matrices and tensor products
⚡ Build, simulate, transpile and run quantum circuits in Python with Qiskit 1.x, Qiskit Aer and Qiskit Runtime
⚡ Implement teleportation, superdense coding, Deutsch-Jozsa, Grover's search, the quantum Fourier transform, phase estimation and Shor's algorithm
⚡ Run experiments on real quantum hardware, model noise, and apply error-mitigation techniques
⚡ Explain the principles of quantum error correction, from repetition codes to surface codes
⚡ Encode classical data into quantum states and build variational quantum classifiers and quantum kernel models with PennyLane and Qiskit Machine Learning
⚡ Train hybrid quantum-classical models with PyTorch, and solve optimisation problems with VQE and QAOA
⚡ Benchmark quantum models honestly against classical baselines and judge claims of quantum advantage
⚡ Complete four portfolio-ready capstone projects and plan a career path in quantum technology

Requirements
❗ Basic Python programming (variables, functions, loops, lists); every library is introduced from scratch
❗ High-school algebra; all linear algebra and complex numbers are taught in Module 2
❗ A computer (Windows, macOS or Linux) with internet access; all software used is free and open source
❗ No prior physics, quantum mechanics or machine learning knowledge is required

Description
This course contains the use of artificial intelligence.

Quantum computing is moving out of the physics lab and onto the developer's laptop. Today you can write a quantum program in Python, run it on a simulator in seconds, and send it to real quantum hardware through the cloud. This course gives you a clear, rigorous and beginner-friendly path from your very first qubit to training hybrid quantum machine learning models.

What makes this course different
✨From zero to advanced in one path. We start with the big picture and the essential mathematics (complex numbers, vectors, matrices, tensor products), then build up to quantum gates, circuits, entanglement, the major algorithms, real hardware and quantum machine learning.

✨Code-first and current. Every concept is backed by Python code using Qiskit 1.x (circuits, primitives, Aer simulation, transpilation, Qiskit Runtime) and PennyLane with PyTorch for quantum machine learning.

✨Honest about what quantum can and cannot do. You will learn to tell real quantum advantage from hype: which speed-ups are exponential, which are quadratic, which are heuristic, and how to benchmark quantum models fairly against strong classical baselines.

✨Real hardware and real noise. You will run circuits on real quantum processors, measure noise, apply error-mitigation techniques, and understand how quantum error correction works.

✨Portfolio-ready capstones. Four capstone projects (an algorithm toolkit, a noise-aware hardware experiment, a hybrid quantum classifier and a QAOA portfolio optimiser) give you work you can show employers.

What's inside
✨ 12 modules, 24 hours of narrated, slide-based video lectures, each 5 to 15 minutes long

✨ 15+ hands-on coding labs and 4 capstone projects

✨ A quiz with detailed answer explanations at the end of every module

✨ Setup guides and code you can run on a free simulator or on free tiers of real quantum hardware

Course structure
✨ Introduction to Quantum Computing

✨ Essential Mathematics and Linear Algebra

✨ Qubits, Superposition, and Measurement

✨ Quantum Gates and Circuits

✨ Python, Qiskit, and Quantum Simulators

✨ Entanglement and Quantum Protocols

✨ Quantum Algorithms: Grover, QFT, and Shor

✨ Real Quantum Hardware, Noise, and Error Correction

✨ Machine Learning Foundations and Data Encoding

✨ Quantum Machine Learning with PennyLane and Qiskit

✨ Hybrid Quantum-Classical AI and Optimization

✨ Capstone Projects and Career Roadmap

No physics degree is required. If you can write basic Python and remember high-school algebra, this course will teach you everything else, step by step.

Who this course is for
⭐ Software developers and Python programmers who want to add quantum computing to their skill set
⭐ Data scientists and machine learning engineers curious about quantum machine learning and hybrid AI
⭐ Students of computer science, physics, mathematics or engineering looking for a practical, code-first introduction
⭐ Researchers and technical professionals who need to evaluate quantum computing for their organisation
⭐ Curious learners who want a rigorous but beginner-friendly path from first principles to advanced topics

Code:

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