arXiv Machine Learning July 24, 2026 By Ashish Kumar Patra, Anurag K. S. V., Ruchika Bhat, Sai Shankar P., Rahul Maitra, Jaiganesh G

Machine-Learned Compact Subspace Generation for Quantum Selected Configuration Interaction within Density Matrix Embedding Framework

Read the original on arXiv Machine Learning →

arXiv:2607. 20585v1 Announce Type: cross Abstract: Sample-based Quantum Diagonalization (SQD), an extension of Quantum Selected Configuration Interaction (QSCI), has emerged as a promising hybrid quantum-classical paradigm for computing molecular ground state energies.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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