High-dimensional geometry imposes concurrency limits on semantic directions in LLM embeddings via residual interference, with N < exp(c d_eff ε²) for coexistence and σ_int ~ √(k/d_eff) for readout noise.
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Fully-connected VQCs match quantum transformer performance on tabular data with far fewer parameters and better noise resilience.
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Semantic Concurrency Limits in Large Language Models
High-dimensional geometry imposes concurrency limits on semantic directions in LLM embeddings via residual interference, with N < exp(c d_eff ε²) for coexistence and σ_int ~ √(k/d_eff) for readout noise.
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Do Quantum Transformers Help? A Systematic VQC Architecture Comparison on Tabular Benchmarks
Fully-connected VQCs match quantum transformer performance on tabular data with far fewer parameters and better noise resilience.