arXiv Machine Learning June 30, 2026 By Anurag K. S. V., Ashish Kumar Patra, Manas Mukherjee, Ruchika Bhat, Sai Shankar P., Rahul Maitra, Jaiganesh G

Bridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular Simulations

Read the original on arXiv Machine Learning →

arXiv:2606. 30551v1 Announce Type: cross Abstract: Calculation of binding energies for protein-ligand molecular systems requires accurate treatment of the electronic structure, a quantum chemistry problem that scales exponentially on classical hardware, while current quantum hardware remains too noisy for the required circuit depths.

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

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

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