AI coding agents evolve simple ground-state protocols into improved versions for VQE, DMRG, and AFQMC on spin models and molecules by using executable energy scores under fixed compute budgets.
Malone, Ankit Mahajan, James S
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
unclear 1representative citing papers
QC-AFQMC per-step scaling reduced from O(N^5.5) to O(N^4.5) via Aitken's block transformation for singular Pfaffians and algorithmic differentiation for force bias, with demonstrations on H8 from real quantum data and Li2O4.
citing papers explorer
-
Optimizing ground state preparation protocols with autoresearch
AI coding agents evolve simple ground-state protocols into improved versions for VQE, DMRG, and AFQMC on spin models and molecules by using executable energy scores under fixed compute budgets.
-
Quantum-Classical Auxiliary-Field Quantum Monte Carlo at the Edge of Practicability
QC-AFQMC per-step scaling reduced from O(N^5.5) to O(N^4.5) via Aitken's block transformation for singular Pfaffians and algorithmic differentiation for force bias, with demonstrations on H8 from real quantum data and Li2O4.