5 papers
Machine-learned syndrome post-selection for reliable quantum error correction
Tobias Haug, Askery Canabarro, Leandro Aolita
Quantum error correction can be enhanced by post-selecting out runs that are likely to produce a logical failure, but the most accurate measures for that require costly decoder-lev…
Distinguishing Ordered Phases using Machine Learning and Classical Shadows
Leandro Morais, Tiago Pernambuco, Rodrigo G. Pereira +3
Classifying phase transitions is a fundamental and complex challenge in condensed matter physics. This work proposes a framework for identifying quantum phase transitions by combin…
A competitive NISQ and qubit-efficient solver for the LABS problem
Marco Sciorilli, Giancarlo Camilo, Thiago O. Maciel +3
Pauli Correlation Encoding (PCE) is as a qubit-efficient variational approach to combinatorial optimization problems. The method offers a polynomial reduction in qubit count and a…
Expressibility, entangling power and quantum average causal effect for causally indefinite circuits
Pedro C. Azado, Guilherme I. Correr, Alexandre Drinko +3
Parameterized quantum circuits are the core of new technologies such as variational quantum algorithms and quantum machine learning, which makes studying its properties a valuable…
Predicting topological invariants and unconventional superconducting pairing from density of states and machine learning
Flavio Noronha, Askery Canabarro, Rafael Chaves +1
Competition between magnetism and superconductivity can lead to unconventional and topological superconductivity. However, the experimental confirmation of the presence of Majorana…