Numerical simulations of a box model for quantum annealing reveal residual energy largely independent of landscape roughness and annealing depth, with flat energy gaps proposed to explain diabatic trapping.
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Spectral softening in driven quadratic systems causes the partition function to diverge and adiabaticity to fail below a finite drive-dependent frequency threshold, rendering equilibrium ill-defined.
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.
A new fusion of adiabatic preconditioning and the Rodeo Algorithm, built hierarchically from solvable subsystems, enables robust exponential convergence for eigenstate preparation in the spin-1/2 XX model at high precision.
citing papers explorer
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Box model of quantum annealing
Numerical simulations of a box model for quantum annealing reveal residual energy largely independent of landscape roughness and annealing depth, with flat energy gaps proposed to explain diabatic trapping.
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Spectral Softening and the Structural Breakdown of Thermodynamic Equilibrium
Spectral softening in driven quadratic systems causes the partition function to diverge and adiabaticity to fail below a finite drive-dependent frequency threshold, rendering equilibrium ill-defined.
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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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Hierarchical Fusion Method for Scalable Quantum Eigenstate Preparation
A new fusion of adiabatic preconditioning and the Rodeo Algorithm, built hierarchically from solvable subsystems, enables robust exponential convergence for eigenstate preparation in the spin-1/2 XX model at high precision.