Efficient subgraph-based sampling of Ising-type models with frustration

Abstract

Here is proposed a general subgraph-based method for efficiently sampling certain graphical models, typically using subgraphs of a fixed treewidth, and also a related method for finding minimum energy (ground) states. In the case of models with frustration, such as the spin glass, evidence is presented that this method can be more efficient than traditional single-site update methods.

References in corpus (4)

• Quantum annealing with more than one hundred qubits
• Optimized simulated annealing for Ising spin glasses
• Glassy Chimeras could be blind to quantum speedup: Designing better benchmarks for quantum annealing machines
• Belief-Propagation Guided Monte-Carlo Sampling

Cited by in corpus (8)

• Benchmarking a quantum annealing processor with the time-to-target metric
• Next-Generation Topology of D-Wave Quantum Processors
• Tropical Tensor Network for Ground States of Spin Glasses
• Enhancing Quantum Annealing Performance for the Molecular Similarity Problem
• Evaluating Ising Processing Units with Integer Programming
• Simulations of Frustrated Ising Hamiltonians with Quantum Approximate Optimization
• Patch-planting spin-glass solution for benchmarking
• Evolutionary Approaches to Optimization Problems in Chimera Topologies