5 courses · 8 tutorials
D-Wave
Learn to identify and describe optimization problems suited for D-Wave's quantum-classical hybrid technology. Covers problem discovery, constraint definition, and building a quantum business case. No math or physics background required.
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's flagship developer training: 35 hours of hands-on instruction covering QUBO formulation, Ising models, Ocean SDK, hybrid solvers, and real-world portfolio optimization. Includes virtual office hours with D-Wave experts. Multiple 2026 sessions available.
D-Wave
D-Wave's bundle combining Quantum Programming Core with one additional month of unlimited Leap Quantum Cloud access. 35 hours of developer training plus hands-on QPU access for first-time Leap users. Approximately 6 weeks total.
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.