The HGP/LP Tanner graph spectral ratio equals (1+β_base)/2 and BB code spectra reduce to lm independent 2×2 SVDs, enabling a multi-layer AOL routing protocol with constant per-cycle depth.
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DMRG calculations find trivial paramagnets on four Archimedean lattices, collinear Neel order on four others, competing phases including a possible spin liquid on the triangular lattice, and a likely Dirac spin liquid on the kagome lattice for the quantum dipolar XY model.
Superimposing a blue-detuned repulsive beam onto an attractive optical tweezer creates a barricade that suppresses light-assisted collision loss, enabling cumulative fill fractions exceeding 80% after four loading cycles for both 87Rb and CaF.
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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Using Tanner Spectral Reduction to Improve Multi-Layer Optical Lattice Routing for Hypergraph-Product and Bivariate Bicycle qLDPC Codes
The HGP/LP Tanner graph spectral ratio equals (1+β_base)/2 and BB code spectra reduce to lm independent 2×2 SVDs, enabling a multi-layer AOL routing protocol with constant per-cycle depth.
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Ground states of quantum XY dipoles on the Archimedean lattices
DMRG calculations find trivial paramagnets on four Archimedean lattices, collinear Neel order on four others, competing phases including a possible spin liquid on the triangular lattice, and a likely Dirac spin liquid on the kagome lattice for the quantum dipolar XY model.
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Near-deterministic loading of optical tweezer arrays via repulsive barricade potentials
Superimposing a blue-detuned repulsive beam onto an attractive optical tweezer creates a barricade that suppresses light-assisted collision loss, enabling cumulative fill fractions exceeding 80% after four loading cycles for both 87Rb and CaF.
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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.