4 courses · 9 tutorials
QUANTUM CIRCUITS
Prof. Elias Fernandez-Combarro Alvarez, University of Oviedo
CERN openlab's free lecture series on quantum computing covering qubits, quantum algorithms, QAOA, VQE, and quantum machine learning. Taught by a University of Oviedo professor with no physics background required.
IBM Quantum
Julien Gacon, Dr. Daniel J. Egger, Dr. Stefan Woerner, Lucia Cuervo Valor (IBM Quantum)
Free openHPI course on quantum approaches to combinatorial optimization, developed with IBM Quantum. Covers QAOA, VQE, and the quadratic unconstrained binary optimization (QUBO) framework with Qiskit implementation.
PennyLane
Xanadu / Community
Xanadu's collection of hundreds of interactive PennyLane tutorials, including 50+ quantum machine learning demos covering VQE, QAOA, quantum kernels, quantum neural networks, and more.
QUANTUM PROGRAMMING
Prof. Dan Boneh and Will Zeng, Stanford
Stanford's CS269Q quantum computer programming course materials from Spring 2019, taught by Dan Boneh and Will Zeng. Lecture slides and project assignments cover pyQuil programming, benchmarking, VQE, QAOA, and error correction.
Implement the Quantum Approximate Optimisation Algorithm to solve the MaxCut graph problem using PennyLane, and understand how QAOA bridges quantum computing and combinatorial optimisation.
Build the Quantum Approximate Optimization Algorithm from scratch in Qiskit to solve MaxCut on small graphs. Understand the circuit structure, cost function, and how to tune the depth parameter p.
Run variational quantum algorithms using Amazon Braket Hybrid Jobs: managed classical compute + quantum access in one job, with checkpointing and cost tracking.
Build a complete Max-Cut solver using QAOA in Qiskit Optimization. Covers graph construction with NetworkX, QuadraticProgram formulation, QAOA via MinimumEigenOptimizer, and how solution quality changes with circuit depth p.
Learn how to build quantum kernel functions with PennyLane, use them with scikit-learn's SVM, and understand when quantum kernels might offer an advantage over classical kernels, with a full working classification example.
Formulate mean-variance portfolio optimization as a QUBO, solve it with QAOA using Qiskit Optimization, and compare results against the classical Markowitz efficient frontier.
Use Qiskit's optimization module to formulate and solve combinatorial problems: Max-Cut, knapsack, and TSP using QAOA and the MinimumEigenOptimizer.
Run QAOA on Max-Cut in PennyLane end to end: encode the graph as a cost Hamiltonian, parameterize the variational circuit, and optimize toward the maximum partition.
Implement the Quantum Approximate Optimization Algorithm to solve a small Max-Cut problem using Cirq, with parameterized circuits and classical optimization.