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…

q-fin.ST2025

Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning

Joshua Rosaler, Luca Candelori, Vahagn Kirakosyan +5

We investigate the application of quantum cognition machine learning (QCML), a novel paradigm for both supervised and unsupervised learning tasks rooted in the mathematical formali…