Quantum computing is consolidating as a relevant tool to address problems where complexity is skyrocketing and the number of possible solutions makes it practically unfeasible even for a classical supercomputer to evaluate them exhaustively. In this context, the quantum computing team at CTIC Quantum Lab is working on exploring these approaches applied to complex optimization problems. It is not used to "do the same thing faster", but to find new ways to do it when the number of combinations, constraints and scenarios makes the problem seem almost unsolvable.
This has application in industrial and strategic sectors: from logistics planning with multiple constraints to the optimization of limited resources, the allocation of tasks in changing environments or the simulation of situations with many variables at stake. These are problems in which a small variation in the environment can force you to recalculate decisions and compare alternatives.
In the field of defense we find an example: how to operate in adverse or hostile conditions, where the rules are constantly changing and the available information is incomplete. A typical case is that of denied GNSS (Global Navigation Satellite System) environments, where systems such as GPS or Galileo are no longer reliable due to interference or signal manipulation. This directly impacts the navigation and coordination of unmanned vehicles (UxVs), such as drones or autonomous land and naval platforms.
Without GNSS the challenge is not only to continue the operation, but to decide how to do it: what routes are viable, how to avoid risky areas, how to maintain communication, how to distribute tasks between units, and how to manage power and mission times. In formal terms, all of this can be modeled as an optimization problem with so many constraints and alternatives that classical calculus can become intractable.
This is where quantum computing fits in as a complementary tool. Not to make real-time decisions during operation, but as part of the planning and improvement cycle: scenarios are defined, behaviors are simulated, strategies are evaluated and mission configurations are optimized by fitting models with data obtained from tests and runs. It is usually combined with classical computing and high-performance simulation (HPC), integrating quantum algorithms in specific optimization subproblems.
This hybrid approach allows tackling problems that, due to their size and complexity, are difficult to deal with using conventional methods, especially when seeking to compare various alternatives and select the best one under uncertain conditions. In this field, CTIC Quantum Lab develops research, evaluation and validation of quantum algorithms applied to complex optimization problems. These activities include the mathematical formulation of industrial and strategic use cases, their adaptation to available quantum hardware, the integration in hybrid architectures together with High Performance Computing (HPC) infrastructures and the comparative analysis against classical methods.
