This approach reduces resource requirements for computing ground-state energies in molecular systems using quantum machine learning and VQE.
We propose a quantum machine learning-based method to compress molecular Hamiltonians and efficiently compute ground-state energies (GSEs) using the variational quantum eigensolver (VQE), significantly reducing resource requirements.
No takes yet. Share an insight, caveat, or question.
Khandelwal et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: