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.
The gurobi optimizer
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
quant-ph 3years
2026 3verdicts
UNVERDICTED 3representative 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.
DEN models were adjusted to embed real-world graphs onto neutral atom QPUs for quantum ML and optimization tasks, achieving partial to full success rates on two hardware platforms.
citing papers explorer
-
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.
-
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.
-
Harnessing DEN models for quantum computing tasks on neutral atom QPUs
DEN models were adjusted to embed real-world graphs onto neutral atom QPUs for quantum ML and optimization tasks, achieving partial to full success rates on two hardware platforms.