A 0505: Quantum Computing Learning Track (5 Weeks)
From Qubits to Quantum Engineering: A Five-Stage Learning Journey
Each course builds on the previous one, moving from basic concepts to real-world quantum engineering skills.
Week 1: Quantum Computing Fundamentals
Goal: Build foundational understanding of quantum mechanics and qubits.
Topics:
Classical vs quantum computing
Qubits, superposition, entanglement
Basic quantum gates and circuits
Quantum measurement and interference
IBM Quantum Composer hands-on labs
Outcome:
Students can understand, build, and simulate simple quantum circuits.
Week 2: Quantum Programming with Qiskit
Goal: Develop quantum programming skills using Qiskit.
Topics:
Installing Qiskit and Jupyter Notebooks
Creating and visualizing quantum circuits in Python
Multi-qubit systems and state vectors
Circuit transpilation and noise simulation
Quantum experiments on real IBMQ devices
Outcome:
Students can write Python code to build and run quantum algorithms on real/simulated devices.
Week 3: Quantum Algorithms and Problem Solving
Goal: Understand and implement famous quantum algorithms.
Topics:
Deutsch-Jozsa, Grover’s, and Shor’s algorithms
Quantum Fourier Transform (QFT)
Quantum phase estimation
Variational Quantum Eigensolver (VQE)
Algorithm design for optimization and search
Outcome:
Students implement working algorithms and understand quantum advantage in computation.
Week 4: Quantum Hardware & Error Correction
Goal: Explore how quantum computers are physically built and stabilized.
Topics:
Superconducting qubits, ion traps, and photonic quantum systems
Qubit decoherence, noise, and fidelity
Quantum error correction codes (bit-flip, phase-flip, Shor code)
Hardware connectivity and circuit optimization
Emerging technologies in quantum devices
Outcome:
Students understand quantum hardware tradeoffs and how to mitigate errors.
Week 5: Quantum Engineering & Applications
Goal: Apply quantum technologies in real-world domains and build scalable systems.
Topics:
Quantum system integration (hardware + software)
Quantum networking and communication
Quantum machine learning (QML) intro
Cloud quantum systems and APIs
Career paths and professional quantum development tools
Capstone Project:
Build and present a complete quantum solution—hardware-aware quantum algorithm or application (e.g., quantum chemistry, cryptography, or finance use case)
Outcome:
Students gain practical quantum engineering experience and are ready to pursue internships, research, or advanced study.