Quantum Optimization – Solve QUBO on D-Wave Quantum Annealer

Solve QUBO on a real D-Wave quantum annealer using Python. In this developer-first tutorial, we solve Max-Cut end to end: D-Wave Leap access, QUBO-to-BQM, QPU submission, SampleSet interpretation, minor embedding, physical-qubit usage, QPU timing, Problem Inspector, and a local simulated-annealing fallback with neal. No quantum-physics background is required. The tutorial is designed for Python developers, computer-science students, optimization practitioners, and anyone who wants to understand what actually happens after calling sampler.sample(...). WHAT YOU WILL LEARN - How to access a D-Wave QPU through Leap - How QUBO, Ising, and Binary Quadratic Models relate - How to use DWaveSampler and EmbeddingComposite - Why a D-Wave QPU returns several samples - How to interpret energy and num_occurrences - How logical variables map to physical qubits - What minor embedding and qubit chains mean - What QPU annealing, sampling, programming, and access times measure - How to test the same workflow locally using neal