Author

Marcos Vinicius Reballo

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Preprint Sep 2026

Quantum Approximate Multi-Objective Optimization in Routing Problems

Multi-objective optimization (MOO) problems are common in logistics, where routing decisions must balance conflicting objectives such as travel distance, delivery time, and operational risk. A recently proposed Quantum Approximate Optimization Algorithm (QAOA) parameter-transfer strategy solves multi-objective MAX-CUT...

E. Lussi, Alisson dos Passos Fumaco, Marcos Vinicius Reballo et al. · 0 citations

Preprint Sep 2026

A Quantum-Inspired Approach to MaxCut Based on Sparse Walsh/Pauli-Correlation Encoding

We present a quantum-inspired Walsh/PCE solver for MaxCut based on sparse Pauli-correlation encodings. Instead of assigning one qubit or one variable to each graph vertex directly, the method represents relaxed binary variables through expectation values of diagonal Pauli/Walsh observables. These correlators are comput...

C. A. Amaral, Marcos Vinicius Reballo, M. Ritt et al. · 0 citations

#machine learning Preprint Aug 2026

A hybrid quantum-classical neural network for learning to route

This work studies hybrid quantum-classical neural networks for learning routing heuristics and identifies encoder feed-forward replacement as a viable hybrid-module compression strategy for neural combinatorial optimization.

M. Ritt, Alexsandro Santos da Rosa, Marcos Vinicius Reballo et al. · 0 citations