Grammatically-Guided Sparse Attention uses POS tags to generate hard or soft masks that constrain self-attention, achieving 0.8200 and 0.8165 accuracy on SST-2 versus 0.8200 for full attention in a DistilBERT-like model.
Floquet quantum simulation with superconducting qubits
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We propose a quantum algorithm for simulating spin models based on periodic modulation of transmon qubits. Using Floquet theory we derive an effective time-averaged Hamiltonian, which is of the general XYZ class, different from the isotropic XY Hamiltonian typically realised by the physical setup. As an example, we provide a simple recipe to construct a transverse Ising Hamiltonian in the Floquet basis. For a 1D system we demonstrate numerically the dynamical simulation of the transverse Ising Hamiltonian and quantum annealing to its ground state. We benchmark the Floquet approach with a digital simulation procedure, and demonstrate that it is advantageous for limited resources and finite anharmonicity of the transmons. The described protocol can serve as a simple yet reliable path towards configurable quantum simulators with currently existing superconducting chips.
fields
cs.CL 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Grammatically-Guided Sparse Attention for Efficient and Interpretable Transformers
Grammatically-Guided Sparse Attention uses POS tags to generate hard or soft masks that constrain self-attention, achieving 0.8200 and 0.8165 accuracy on SST-2 versus 0.8200 for full attention in a DistilBERT-like model.