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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The paper introduces the first general protocol for magnetic hysteresis on programmable quantum annealers and reports non-monotonic dependence of loop area on quantum fluctuations along with disorder-induced steps.
SBQA adds inter-replica interactions to simulated bifurcation to mimic quantum tunneling and improves performance on sparse rugged optimization problems over standard SBM.
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.
Supervised ML trained on simulated gate set tomography data predicts noise models to build cross-hardware quantum emulators, validated by matching H2 unitary coupled cluster energy results to real hardware within 0.128% relative error.
VeloxQ is a classical QUBO solver that reports competitive or superior performance and unique scalability to 10^8-variable sparse instances across benchmarks against quantum annealers, physics-inspired methods, and conventional solvers.
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
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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.
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Magnetic Hysteresis Experiments Performed on Quantum Annealers
The paper introduces the first general protocol for magnetic hysteresis on programmable quantum annealers and reports non-monotonic dependence of loop area on quantum fluctuations along with disorder-induced steps.
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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.
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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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Designing a Machine Learning-Driven, Cross-Hardware Emulator for Noisy Quantum Computers with Gate-Based Protocols
Supervised ML trained on simulated gate set tomography data predicts noise models to build cross-hardware quantum emulators, validated by matching H2 unitary coupled cluster energy results to real hardware within 0.128% relative error.
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VeloxQ: A Fast and Efficient QUBO Solver
VeloxQ is a classical QUBO solver that reports competitive or superior performance and unique scalability to 10^8-variable sparse instances across benchmarks against quantum annealers, physics-inspired methods, and conventional solvers.
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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.