A modified autoencoder with a custom embedding loss learns spatial mappings to solve the constrained unit disk problem for qubit embedding on neutral-atom quantum processors and outperforms classical solvers under fixed computation time.
Solving optimization problems with rydberg analog quantum computers: Realistic requirements for quantum advantage using noisy simulation and classical benchmarks
2 Pith papers cite this work. Polarity classification is still indexing.
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quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
BBQ-mIS decomposes graph coloring into parallel maximum independent set instances on Rydberg quantum hardware combined with classical branch-and-bound to produce proper colorings with few colors.
citing papers explorer
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Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks
A modified autoencoder with a custom embedding loss learns spatial mappings to solve the constrained unit disk problem for qubit embedding on neutral-atom quantum processors and outperforms classical solvers under fixed computation time.
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BBQ-mIS: a parallel quantum algorithm for graph coloring problems
BBQ-mIS decomposes graph coloring into parallel maximum independent set instances on Rydberg quantum hardware combined with classical branch-and-bound to produce proper colorings with few colors.