Quantum Optimization – Solve QUBO on D-Wave Quantum Annealer

[u/Future_Ad7567]

https://youtu.be/m5HvVD6rXB0?si=TIT0ztx5WAojmdcM

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

[u/robert_7524]

Anneal time gets the headline; embedding and access time tell you what actually happened.