Ember provides the first standardized, reproducible benchmark framework with 24,016 diverse graph instances for quantum annealing embedding algorithms, showing that no single algorithm performs best across all graph families.
Gomez-Tejedor, E
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
quant-ph 5verdicts
UNVERDICTED 5roles
background 2polarities
background 2representative citing papers
SBQA adds inter-replica interactions to simulated bifurcation to mimic quantum tunneling and improves performance on sparse rugged optimization problems over standard SBM.
Compares quantum annealing models for coarse-grained protein folding, proposes interleaved-grid tetrahedral encoding, and reports hardware limits from embedding alongside scaling gains over classical simulated annealing on embedded instances.
The thesis introduces a topology-aware tensor-network heuristic called SpinGlassPEPS.jl and thermodynamic metrics to benchmark quantum annealers on Ising problems while accounting for dissipation and effective temperature.
Quantum annealing is described as a heuristic for discrete optimization and sampling that also serves as a platform for studying non-equilibrium many-body quantum dynamics with programmable spin systems.
citing papers explorer
-
Ember: An Extensible Benchmark Suite for Quantum Annealing Embedding Algorithms
Ember provides the first standardized, reproducible benchmark framework with 24,016 diverse graph instances for quantum annealing embedding algorithms, showing that no single algorithm performs best across all graph families.
-
Simulated Bifurcation Quantum Annealing
SBQA adds inter-replica interactions to simulated bifurcation to mimic quantum tunneling and improves performance on sparse rugged optimization problems over standard SBM.
-
Exploring Quantum Annealing for Coarse-Grained Protein Folding
Compares quantum annealing models for coarse-grained protein folding, proposes interleaved-grid tetrahedral encoding, and reports hardware limits from embedding alongside scaling gains over classical simulated annealing on embedded instances.
-
Neural and Tensor Networks in the Study of Quantum Annealing Processors
The thesis introduces a topology-aware tensor-network heuristic called SpinGlassPEPS.jl and thermodynamic metrics to benchmark quantum annealers on Ising problems while accounting for dissipation and effective temperature.
-
Quantum Annealing: Optimisation, Sampling, and Many-Body Dynamics
Quantum annealing is described as a heuristic for discrete optimization and sampling that also serves as a platform for studying non-equilibrium many-body quantum dynamics with programmable spin systems.