Quantum optimization for complex system design and control

Design and/or implementation of complex control systems often require to solve hard non-convex optimization problems, involving large numbers of decision variables. In several situations, these problems are characterized by an exponential computational complexity, making their solution unfeasible using classical optimization algorithms. In recent years, it has been shown that quantum computers can significantly reduce the computational complexity of certain classes of non-convex optimization problems, allowing their solution in polynomial time.

The aim of this research is to develop general methodologies, allowing the formulation of non-convex optimization problems in a form suitable for quantum computers.

These methodologies are being employed in different areas, such as nonlinear model predictive control, complex dynamic networks, machine learning, decision making and verification, with applications in the aerospace, automotive, energy and environmental fields.

Erc Sector:

  • PE7_1 Control engineering
  • PE7_3 Simulation engineering and modelling
  • PE1_19 Control theory and optimisation


  • Quantum computing
  • Control design
  • Optimization
  • Complex Systems

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