QUBO (Quadratic Unconstrained Binary Optimization)

Definition:

QUBO refers to a mathematical model used to represent complex optimization problems in a standardized form that can be efficiently solved using specialized algorithms or hardware (e.g., quantum annealers, heuristics, or classical solvers). The QUBO formulation encodes a problem into a quadratic objective function involving binary variables (0 or 1), without explicit constraints. All constraints are instead embedded into the objective through penalty terms.

Applications in Operations Research:

QUBO models are especially useful in combinatorial optimization scenarios such as workforce scheduling, vehicle routing, task assignment, and supply chain optimization. By reformulating traditional constraint-based problems into QUBO, Solvice and similar platforms can tap into highly scalable solving techniques, including those compatible with quantum computing and advanced metaheuristics.

Benefits:

  • Allows embedding of constraints directly into the objective function.
  • Ideal for encoding NP-hard problems common in operations research.
  • Compatible with a wide range of solvers, including emerging quantum technologies.

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