10 citations · 10 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
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…
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…
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…