activity
20242026
collaborators

5 papers

quant-ph2026

Shallow-circuit Supervised Learning on a Quantum Processor

Luca Candelori, Swarnadeep Majumder, Antonio Mezzacapo +6

Quantum computing has long promised transformative advances in data analysis, yet practical quantum machine learning has remained elusive due to fundamental obstacles such as a ste…

cs.LG2025

Quantum Geometry of Data

Alexander G. Abanov, Luca Candelori, Harold C. Steinacker +9

We demonstrate how Quantum Cognition Machine Learning (QCML) encodes data as quantum geometry. In QCML, features of the data are represented by learned Hermitian matrices, and data…

q-bio.QM2025

Quantum Cognition Machine Learning for Forecasting Chromosomal Instability

Giuseppe Di Caro, Vahagn Kirakosyan, Alexander G. Abanov +11

The accurate prediction of chromosomal instability from the morphology of circulating tumor cells (CTCs) enables real-time detection of CTCs with high metastatic potential in the c…

q-fin.ST2025

Supervised Similarity for Firm Linkages

Ryan Samson, Adrian Banner, Luca Candelori +9

We introduce a novel proxy for firm linkages, Characteristic Vector Linkages (CVLs). We use this concept to estimate firm linkages, first through Euclidean similarity, and then by…

stat.ML2024

Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning

Luca Candelori, Alexander G. Abanov, Jeffrey Berger +8

We propose a new data representation method based on Quantum Cognition Machine Learning and apply it to manifold learning, specifically to the estimation of intrinsic dimension of…