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.
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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.