5 papers
Quantum Approximate Multi-Objective Optimization in Routing Problems
Eduardo Willwock Lussi, Alisson dos Passos Fumaco, Marcos Vinicius Reballo +3
Multi-objective optimization (MOO) problems are common in logistics, where routing decisions must balance conflicting objectives such as travel distance, delivery time, and operati…
Sparse Walsh-Pauli Correlation Encoding for MaxCut: A Quantum-Inspired Formulation with Quantum-Hardware Validation
Cesar Augusto do Amaral, Nicolás Legnazzi, Marcos Vinicius Reballo +3
We introduce a sparse Walsh-Pauli correlation encoding for MaxCut, in which graph variables are represented by diagonal Pauli-string expectation values computed classically as norm…
A hybrid quantum-classical neural network for learning to route
Marcus Rolf Peter Ritt, Alexsandro Santos da Rosa Júnior, Marcos Vinicius Reballo +2
This work studies hybrid quantum-classical neural networks for learning routing heuristics. Specifically, this paper asks whether small quantum neural networks can replace paramete…
Evaluating Parameter Transfer in FALQON Across Graph Families
Alisson dos Passos Fumaco, Marcos Vinicius Reballo, Fernando Augusto Caletti de Barros +2
We evaluate FALQON parameter transfer for Max-Cut, transferring sequences from small donors () to 14-node recipients. Using 3-regular and Erdős-Rényi families, w…
Accelerating State-Vector Quantum Simulation on Integrated GPUs via Cache Locality Optimization: A Cross-Architecture Evaluation
Gabriel Fernandes Thomaz, Jerusa Marchi, Eduarda Rodrigues Monteiro +2
The classical simulation of quantum algorithms is a crucial tool for circuit development, testing, and validation. Although acceleration using GPUs significantly reduces simulation…