5 courses · 9 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 free introductory course demystifying quantum computing for general audiences. Covers how quantum computers work, annealing vs gate-model systems, and real-world optimization use cases. No background required.
PennyLane
Xanadu / PennyLane Team
Interactive browser-based coding exercises teaching quantum computing with PennyLane from scratch, spanning 15 modules from qubits and gates through Grover's and Shor's algorithms, error correction, and variational quantum algorithms.
edX
Pramey Upadhyaya
The algorithms-and-software course closing Purdue University's Applied Quantum Computing series. Covers the quantum Fourier transform, search and Shor's algorithm, plus modern algorithms for optimization, simulation, chemistry, and machine learning, with hands-on cloud hardware access.
Use D-Wave's Constrained Quadratic Model (CQM) sampler to solve optimization problems with explicit constraints, eliminating the need for manual QUBO penalty encoding.
How Qiskit's transpiler transforms abstract circuits into hardware-executable form: qubit mapping, SWAP insertion, gate decomposition, and optimization passes.
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.
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.
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.