A Pauli-transfer-matrix analysis of QELMs reveals the full set of nonlinear Pauli features generated by encoding and transformed by quantum channels, producing an interpretable classical nonlinear vector autoregression model that approximates flow maps in dynamical systems.
Quantum kitchen sinks: An algorithm for machine learning on near-term quantum computers
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Quantum-inspired 1024-D document embeddings exhibit weak, unstable ranking performance and structural geometric limitations, performing better as auxiliary components in hybrid lexical-embedding retrieval systems.
A mixture-of-experts hybrid quantum model achieves 0.793 average precision on credit card fraud detection compared to 0.770 for XGBoost, with modest extra inference time.
citing papers explorer
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Theory and interpretability of Quantum Extreme Learning Machines: a Pauli-transfer matrix approach
A Pauli-transfer-matrix analysis of QELMs reveals the full set of nonlinear Pauli features generated by encoding and transformed by quantum channels, producing an interpretable classical nonlinear vector autoregression model that approximates flow maps in dynamical systems.
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On the Representational Limits of Quantum-Inspired 1024-D Document Embeddings: An Experimental Evaluation Framework
Quantum-inspired 1024-D document embeddings exhibit weak, unstable ranking performance and structural geometric limitations, performing better as auxiliary components in hybrid lexical-embedding retrieval systems.
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A Mixture-of-Experts Framework for Practical Hybrid-Quantum Models in Credit Card Fraud Detection
A mixture-of-experts hybrid quantum model achieves 0.793 average precision on credit card fraud detection compared to 0.770 for XGBoost, with modest extra inference time.