Quantum Computing & Machine Learning Build with QiskitPublished 8/2026
Created by Soumya Ganguly
MP4 |
Video: h264, 1280x720 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate |
Genre: eLearning |
Language: English + subtitle |
Duration: 151 Lectures ( 11h 23m ) |
Size: 1.2 GB
⚡ A real quantum toolkit. Set up Python, Jupyter, Qiskit, and PennyLane, and learn the modern Qiskit workflow
⚡ Gates and circuits from scratch
⚡ The famous algorithms, implemented
⚡ Variational quantum computing
⚡ Error and noise
⚡ Quantum machine learning, hands-on
Requirements❗ Comfort with basic Python and high-school math. No prior Qiskit experience needed - we install and set up everything together. A free IBM Quantum account lets you run on real hardware.
DescriptionThis course contains the use of artificial intelligence.
You understand what a qubit is. Now it's time to build with one.This is the intermediate, coding-first course that turns quantum curiosity into real, working code. Over 19 sections and 99 lessons - around 11 hours of video, with 52 hands-on labs - you'll write and run genuine quantum programs in Python and Qiskit, from your very first Bell state to a complete quantum machine learning capstone.
We don't just talk about algorithms; we build them. You'll implement Deutsch-Jozsa, Bernstein-Vazirani, Simon's, Grover's search, the Quantum Fourier Transform, phase estimation, and a small run of Shor's algorithm - line by line, then run them on a simulator and watch the results appear. Every hands-on lab comes with a short companion walkthrough clip showing the exact code, circuit, and output, so nothing stays abstract.
What you'll build and learn•
A real quantum toolkit.Set up Python, Jupyter, Qiskit, and PennyLane, and learn the modern Qiskit workflow: build, transpile, verify, run - including on real IBM hardware.
•
Gates and circuits from scratch.Pauli, Hadamard, phase, and rotation gates; multi-qubit entanglers; the Bloch sphere in code; measurement, shots, and reading results.
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The famous algorithms, implemented.Quantum parallelism and phase kickback, then Deutsch-Jozsa, Bernstein-Vazirani, Simon's, Grover's, QFT, phase estimation, and Shor's - as code you run.
•
Variational quantum computing.QAOA for Max-Cut and VQE for the H₂ molecule, plus the optimizers, benchmarking, and NISQ-era reality behind them.
•
Error and noise.Model noise with Qiskit Aer and build the bit-flip, phase-flip, and Shor codes; apply readout-error and ZNE mitigation.
•
Quantum machine learning, hands-on.Data encoding and feature maps, a variational quantum classifier you train and debug, quantum kernels, and a full end-to-end QML capstone benchmarked against a classical baseline.
You'll also get a resource-and-quiz sheet with every lesson, plus a companion demo clip for all 52 labs - so you can watch it, then do it yourself.
Who this course is for⭐ Learners who finished a beginner quantum course (or already know the basics) and want to actually build.
⭐ Python developers and data scientists moving into quantum computing and quantum machine learning.
⭐ Students and researchers who want practical Qiskit and PennyLane skills, not just theory.
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