Comment on "Supervised Learning with Quantum Hamiltonian Embeddings" (arXiv:2610.26833)
Alexander G. Abanov, Luca Candelori, Kharen Musaelian
Abstract
We discuss the relationship between quantum Hamiltonian embeddings (arXiv:2610.26833) and prior developments in Quantum Cognition Machine Learning (QCML), including QCML's earlier implementation on IBM quantum hardware. We consider the novelty claims and the computational role of encoding an entire dataset in a single ground state.
Create a lesson
Related papers
Data informed Ansatz Selection for Variational Quantum Circuits
Manav Seksaria, Advith Desu, Anil Prabhakar
Operational Thresholds for Long-Range Entanglement in Quantum Networks
Arn Caesar, Xinqi Hu, Kim Christensen
QECert: Formal Foundations for Quantum Error-Correction Theory in Lean
Keming He, Jiahan Chen, Enji Xiong et al.
Reopening the one clean qubit case of a semi-grand challenge
Christopher W. Mansell
Recoverability of Gravitational Entanglement under Decoherence
Yi-Xuan Wang, Julen S. Pedernales, Martin B. Plenio
On Revolutionizing Power Systems: Suitability of Quantum Computing-Ready Pipelines for Stochastic Unit Commitment Optimization
Ying-Yi Hong, Dylan Josh Domingo Lopez