Topic
5 courses · 13 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's free introduction. How annealing machines differ from gate-model computers, and which optimization problems they are sold for. No background 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.
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.
Use D-Wave's Constrained Quadratic Model (CQM) sampler to solve optimization problems with explicit constraints, eliminating the need for manual QUBO penalty encoding.
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.
Learn how logical problem variables map to physical qubits through minor-embedding, and how to diagnose and fix chain breaks on D-Wave hardware.
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.
Learn the full D-Wave hybrid workflow: define optimization problems as BQMs and CQMs, use LeapHybridSampler for large-scale solving, apply SteepestDescentSolver for local refinement, and decide when hybrid outperforms pure classical or QPU-only approaches.
Solve large optimization problems with D-Wave Leap hybrid solvers that combine classical heuristics and quantum annealing.
Learn how to formulate combinatorial optimization problems as QUBO matrices for D-Wave, including penalty terms for constraints and a worked number partitioning example.
A clear comparison of quantum annealing and gate-based quantum computing: how they work, what problems they solve best, and when to choose each approach.
Use D-Wave Ocean's SimulatedAnnealingSampler to test QUBO formulations locally before committing to quantum hardware.
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.