TNPA uses tensor-network contractions only in a reliable temperature window to seed population annealing, with an effective-sample-size diagnostic to pick the switch-over temperature.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2roles
background 1polarities
background 1representative citing papers
Deep Boltzmann Quantum States with natural-gradient optimization and annealing-like training match exact or best-known solutions for large infinite-range Ising spin glasses and solve job shop scheduling instances.
citing papers explorer
-
Tensor-Network Population Annealing
TNPA uses tensor-network contractions only in a reliable temperature window to seed population annealing, with an effective-sample-size diagnostic to pick the switch-over temperature.
-
Solving Classical and Quantum Spin Glasses with Deep Boltzmann Quantum States
Deep Boltzmann Quantum States with natural-gradient optimization and annealing-like training match exact or best-known solutions for large infinite-range Ising spin glasses and solve job shop scheduling instances.