Topic
6 courses · 8 tutorials
D-Wave
Preparatory course building the math and Python skills needed before D-Wave's Quantum Programming Core. Covers QUBO formulation, quadratic models, constraint writing, and Python for optimization. Optional but recommended for developers without strong math backgrounds.
D-Wave
D-Wave course on spotting optimization problems their hybrid solvers can take. Constraints and a business case. No maths or physics required.
D-Wave
D-Wave developer training, about 35 hours: QUBO, Ising models, the Ocean SDK, hybrid solvers, and a portfolio example. Office hours with D-Wave staff. Several 2026 dates.
D-Wave
Quantum Programming Core plus one extra month of Leap cloud access. About 35 hours, then QPU time, for people new to Leap. About six weeks.
QURECA
QURECA's 25-hour advanced QML course, listed with QTIndu. QUBO and D-Wave, parameterised circuits, QAOA, classifiers and clustering. 1,000 EUR on their academy at the time of writing.
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.
Learn how to formulate the Traveling Salesman Problem and graph coloring as QUBOs and BQMs using D-Wave Ocean SDK, then solve them with LeapHybridSampler and interpret the results.
Formulate the graph coloring problem as a QUBO and solve it on D-Wave using the Ocean SDK.
Write your first quantum annealing program with D-Wave Ocean, formulate a simple QUBO problem and solve it with the simulated annealing sampler.
Install the D-Wave Ocean SDK, formulate a simple optimization problem as a QUBO, and solve it using D-Wave's quantum annealer or simulator.
Use D-Wave's Leap hybrid solver service to solve large optimization problems that are too big for the QPU alone: LeapHybridSampler, problem formulation, and interpreting solutions.
Use Qiskit's optimization module to formulate and solve combinatorial problems: Max-Cut, knapsack, and TSP using QAOA and the MinimumEigenOptimizer.
Learn how to formulate combinatorial optimization problems as QUBO matrices for D-Wave, including penalty terms for constraints and a worked number partitioning example.
Learn D-Wave's Ocean SDK for quantum annealing, formulate combinatorial optimisation problems as QUBO models and solve them on the world's largest quantum annealing processors.