15 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…
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…
Learning spectral density functions in open quantum systems
Felipe Peleteiro, João Victor Shiguetsugo Kawanami Lima, Pedro Marcelo Prado +2
Spectral density functions quantify how environmental modes couple to quantum systems and govern their open dynamics. Inferring such frequency-dependent functions from time-domain…