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.
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A practical heuristic for finding graph minors
11 Pith papers cite this work. Polarity classification is still indexing.
11
Pith papers citing it
abstract
We present a heuristic algorithm for finding a graph $H$ as a minor of a graph $G$ that is practical for sparse $G$ and $H$ with hundreds of vertices. We also explain the practical importance of finding graph minors in mapping quadratic pseudo-boolean optimization problems onto an adiabatic quantum annealer.
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representative citing papers
All minimal non-apex graphs with 12 or fewer vertices or 26 or fewer edges are listed, and every 13-vertex graph with minimum degree 6 is shown to be apex or to contain a K6 minor.
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.
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.
A D-Wave quantum annealer programmed only with the rules of tic-tac-toe sampled future game paths to choose moves and beat a random opponent in 56 of 60 games.
A wedding-seating benchmark shows D-Wave's quantum annealer returns invalid seat assignments on all but trivial problems, while classical Monte Carlo solves all of them; the benchmark is released as open source.
MTQA embeds multiple NP-hard problems such as minimum vertex cover and graph partitioning into spatially distinct regions on quantum hardware, delivering comparable solution quality to single-task annealing with reduced time-to-solution.
Neural networks transform initial embeddings into feasible unit disk configurations for QUBO problems on Rydberg qubits and outperform the Gurobi solver in experiments.
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.
citing papers explorer
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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.
-
New minor minimal non-apex graphs
All minimal non-apex graphs with 12 or fewer vertices or 26 or fewer edges are listed, and every 13-vertex graph with minimum degree 6 is shown to be apex or to contain a K6 minor.
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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.
-
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.
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Playing Dice with the Universe: Programming Quantum Computers to Play Traditional Games
A D-Wave quantum annealer programmed only with the rules of tic-tac-toe sampled future game paths to choose moves and beat a random opponent in 56 of 60 games.
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Entangled happily ever after: Wedding reception seating mapped to classical and quantum optimizers
A wedding-seating benchmark shows D-Wave's quantum annealer returns invalid seat assignments on all but trivial problems, while classical Monte Carlo solves all of them; the benchmark is released as open source.
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Multi-tasking through quantum annealing
MTQA embeds multiple NP-hard problems such as minimum vertex cover and graph partitioning into spatially distinct regions on quantum hardware, delivering comparable solution quality to single-task annealing with reduced time-to-solution.
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Neural-powered unit disk graph embedding: qubits connectivity for some QUBO problems
Neural networks transform initial embeddings into feasible unit disk configurations for QUBO problems on Rydberg qubits and outperform the Gurobi solver in experiments.
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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.
- A calibrated diagnostic for reverse-anneal sampling in programmable quantum annealers
- Schedule-dependent basin occupation and instance-specific effective scales in cycled reverse annealing on programmable quantum annealers