4 papers
Robust Pretty Good Measurement via Hybrid Classical-Quantum Pseudoinverse Approximation and Circuit-Level Realization
Bikash K. Behera, Andrés Camilo Granda Arango, Giuseppe Sergioli +1
Pretty Good Measurement (PGM) is a near-optimal strategy for quantum state discrimination, but its practical realization becomes unstable when the ensemble operator is singular or…
QSMOTE-PGM/kPGM: QSMOTE Based PGM and kPGM for Imbalanced Dataset Classification
Bikash K. Behera, Giuseppe Sergioli, Roberto Giuntini
Quantum-inspired machine learning (QiML) employs mathematical principles from quantum theory, such as Hilbert-space representations and quantum state discrimination, to enhance cla…
Pretty Good Measurement for Radiomics: A Quantum-Inspired Multi-Class Classifier for Lung Cancer Subtyping and Prostate Cancer Risk Stratification
Giuseppe Sergioli, Carlo Cuccu, Giovanni Pasini +4
We investigate a quantum-inspired approach to supervised multi-class classification based on the Pretty Good Measurement (PGM), viewed as an operator-valued decision rule derived f…
Distribution of Non-Locality On Quantum Random Circuits
Andrés Camilo Granda Arango, Federico Hernán Holik, Roberto Giuntini +2
In this work we explore how different types of resources are distributed among the states generated by quantum random circuits (QRC). We focus on multipartite non-locality, but we…