548 citations · 3.8k across the 69 of their papers we have counts for
68 papers · 1 filter
Generalized two-qubit Hamiltonian for Projective Quantum Feature Maps
Rafael Simões do Carmo, Edson Amaro Junior, Felipe Fanchini
Projected quantum feature maps provide a strategy for using quantum processors as feature generators for classical machine-learning models. Building on counterdiabatic Ising-glass…
Quantum Optimization Algorithms for Strongly Correlated Many-Body Systems
G. E. L. Pexe, L. A. M. Rattighieri, P. M. Prado +2
This perspective article analyzes the potential and critical challenges of employing quantum optimization algorithms to investigate phase transitions in quantum many-body systems d…
PUBO Formulation for MST and Application to Optimum-Path Forest
Guilherme E. L. Pexe, Lucas A. M. Rattighieri, Leandro A. Passos +5
The Optimum-Path Forest is a graph-based framework for designing classifiers that exploit inter-sample connectivity. A particular variant constructs decision boundaries based on pr…
From Foundation ECG Models to NISQ Learners: Distilling ECGFounder into a VQC Student
Giovanni dos Santos Franco, Felipe Mahlow, Ellison Fernando Cardoso +1
Foundation models have recently improved electrocardiogram (ECG) representation learning, but their deployment can be limited by computational cost and latency constraints. In this…
Quantum feedback algorithms for DNA assembly using FALQON variants
Pedro M. Prado, Lucas A. M. Rattighieri, Rafael Simões do Carmo +6
Reconstructing DNA sequences without a reference, known as de novo assembly, is a complex computational task involving the alignment of overlapping fragments. To address this probl…
Measurement-Guided State Refinement for Shallow Feedback-Based Quantum Optimization Algorithm
Lucas A. M. Rattighieri, Pedro M. Prado, Marcos C. de Oliveira +1
Limited circuit depth remains a central constraint for quantum optimization in the noisy intermediate-scale quantum (NISQ) regime, where shallow unitary dynamics may fail to suffic…