SPADE is a split-and-delay embedding technique for multi-feature autoregressive transformers that achieves competitive performance on high-granularity calorimeter shower simulation.
AllShowers: One model for all calorimeter showers,
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Presents CaloTrilogy, a unified one-step generative model for high-granularity calorimeter showers that combines velocity field integration, learned priors, and physics losses to match SOTA quality.
An IQP Born machine with Mixture-of-IQP architecture and Pearson-Stabilized Correlation Kernel is trained on calorimeter images at 64 qubits and compiled to a single IQP circuit, reporting MAE_rho of 0.069 versus baseline 0.100.
citing papers explorer
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SPADE: Split-and-Delay Embeddings for Autoregressive High-Granularity Calorimeter Simulation
SPADE is a split-and-delay embedding technique for multi-feature autoregressive transformers that achieves competitive performance on high-granularity calorimeter shower simulation.
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CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
Presents CaloTrilogy, a unified one-step generative model for high-granularity calorimeter showers that combines velocity field integration, learned priors, and physics losses to match SOTA quality.
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An IQP Born Machine for Calorimeter Image Generation at 64 Qubits with Compiled-IQP Deployment
An IQP Born machine with Mixture-of-IQP architecture and Pearson-Stabilized Correlation Kernel is trained on calorimeter images at 64 qubits and compiled to a single IQP circuit, reporting MAE_rho of 0.069 versus baseline 0.100.