Can a carefully chosen quantum subproblem improve a constrained decision without hiding the cost of the surrounding system?
The research thesis
My proposed approach begins with a graph: tasks, dependencies, resources and forbidden combinations. A small part of the problem can then be expressed as a discrete optimization objective and evaluated with classical search, quantum-inspired solvers or a quantum algorithm.
Feasibility matters as much as objective value. A mathematically attractive solution is unusable if it breaks safety, timing or resource constraints. The evaluation should retain these constraints from the original application.
An experiment you can explore now
The open laboratory on this site implements a four-qubit, one-layer QAOA statevector simulation for a five-edge MaxCut problem. Changing two angles changes interference and the probabilities of the sixteen possible configurations.
An exhaustive classical baseline provides the exact optimum for this small graph. The demonstration makes the algorithm inspectable; it is an educational experiment on a classical computer, with no claim of computational advantage.
A next pilot
Choose a small scheduling or allocation task, document its constraints and test the same objective across competing solvers. Record feasible-solution rate, objective quality, variability and total time.
A partner would receive an executable problem definition and a comparative report showing where quantum, quantum-inspired or classical methods deserve further investigation.
Scientific connection
This direction connects the Robotics focus on autonomous systems with scientific computation and the public literature on quantum optimization. The research begins with a measurable decision problem and a defined experiment.
Sources & scientific context
Author
Maurizio VivianiIndependent research · Robotics
Take one question into an experiment.
Discuss a focused research collaboration, benchmark or implementation study.
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