Quantum Annealing for Combinatorial Optimization

A guided research project in Quantum Computing.

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Question

How can a discrete optimization problem be encoded as the lowest energy of a quantum annealer?

Optimization becomes a search for the binary state with the smallest designed energy.
Optimization becomes a search for the binary state with the smallest designed energy.

Goals

  • Convert a chosen combinatorial problem into an Ising or QUBO model.
  • Compare annealing results with a classical solution method.

Method

Make low-energy binary states represent good solutions and high-energy states represent violations.

  • Define binary variables, the objective, and penalty terms.
  • Map the model to available qubits and choose penalty or chain strengths.
  • Sample solutions and compare validity, quality, and running time.