8 papers
Preparing a Thermofield Double State with Feedback Quantum Algorithms
Guilherme E. L. Pexe, Lucas A. M. Rattighieri, Felipe F. Fanchini +2
The efficient preparation of correlated thermal states, such as the Thermofield Double (TFD) state, is a fundamental prerequisite for simulating quantum gravity models and many-bod…
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…
FALQON-MST: A Fully Quantum Framework for Graph Optimization in Vision Systems
Guilherme E. L. Pexe, Lucas A. M. Rattighieri, Leandro A. Passos +4
Finding the minimum spanning tree (MST) of a graph is an important task in computer vision, as it enables a sparse and low-cost representation of connectivity among elements (such…
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…
Quantum Phases Classification Using Quantum Machine Learning with SHAP-Driven Feature Selection
Giovanni S. Franco, Felipe Mahlow, Pedro M. Prado +3
In this study, we present an innovative methodology to classify quantum phases within the ANNNI (Axial Next-Nearest Neighbor Ising) model by combining Quantum Machine Learning (QML…