Quantum Computing & Machine Learning Build with Qiskit
Published 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

Go from theory to code: build real quantum circuits, algorithms & quantum machine learning models in Python & Qiskit.
[h2]What you'll learn[/h2]
⚡ 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
[h2]Requirements[/h2]
❗ 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.
[h2]Description[/h2]
This 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.
•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.
[h2]Who this course is for[/h2]
⭐ 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.
[h2]Homepage[/h2]

https://www.udemy.com/course/quantum-computing-machine-learning-build-with-qiskit

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