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.
Bakalov, Frédéric Sauvage, Alexander F
6 Pith papers cite this work. Polarity classification is still indexing.
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Fragment classification is efficiently learnable by quantum neural networks under suitable conditions but resists known classical dequantization techniques.
The work constructs a permutation-equivariant quantum GNN that implements message passing at selectable Weisfeiler-Leman levels, supports pre-training on small graphs, and demonstrates readout scalability with simulations up to 56 qubits on synthetic, molecular, and TSP datasets.
CV-ADAPT-VQE with tailored symmetry-preserving pools achieves significantly shallower circuits than Hamiltonian-based VQE for bosonic lattice models in GPU classical simulations.
QTL unifies expectation-value minimization with CVaR and Gibbs heuristics under one tunable operator, amplifying gradients in structured cases while preserving global minima and shifting the bottleneck to measurement variance.
A necessary condition for variational quantum circuits to reach exact ground states requires matching module projection norms between input and solution, enabling classical O(n^5) exact solvers for problems like MaxCut.
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Fragmentation is Efficiently Learnable by Quantum Neural Networks
Fragment classification is efficiently learnable by quantum neural networks under suitable conditions but resists known classical dequantization techniques.