QuPort introduces a three-level graph model and TPCCAP optimizer for compiling circuits on modular multi-QPU systems while balancing topology, port usage, and link congestion.
SIAM Journal on Scientific Computing20(1), 359–392 (1998)
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CSV-ViT proposes ROI-preserving variable-sized cortical supervertices and a mask-aware ViT to classify AD-related statuses from T1 MRI, reporting higher performance than recent surface models.
ShardTensor is a domain-parallelism system for SciML that enables flexible scaling of extreme-resolution spatial datasets by removing the constraint of batch size one per device.
A GNN framework learns spectral embeddings of sparse matrices to minimize a fill-in surrogate and produces competitive reorderings versus classical graph algorithms.
A time-aware beam search partitions quantum circuits across QPUs with quadratic scaling in qubits and lower communication overhead than static baselines.
Matching-based AMG preconditioners deliver robust and scalable performance for solving large ill-conditioned systems from IgA discretizations in parallel HPC settings.
citing papers explorer
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QuPort: Topology-, Port-, and Congestion-Aware Compilation for Modular Multi-QPU Quantum Systems
QuPort introduces a three-level graph model and TPCCAP optimizer for compiling circuits on modular multi-QPU systems while balancing topology, port usage, and link congestion.
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CSV-ViT: A Vision Transformer with the Variable-sized Cortical Supervertices for Detection of Alzheimer's Disease Pathologies
CSV-ViT proposes ROI-preserving variable-sized cortical supervertices and a mask-aware ViT to classify AD-related statuses from T1 MRI, reporting higher performance than recent surface models.
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ShardTensor: Domain Parallelism for Scientific Machine Learning
ShardTensor is a domain-parallelism system for SciML that enables flexible scaling of extreme-resolution spatial datasets by removing the constraint of batch size one per device.
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Bridging the Gap between Sparse Matrix Reordering and Factorization: A Deep Learning Framework for Fill-in Reduction
A GNN framework learns spectral embeddings of sparse matrices to minimize a fill-in surrogate and produces competitive reorderings versus classical graph algorithms.
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Efficient Time-Aware Partitioning of Quantum Circuits for Distributed Quantum Computing
A time-aware beam search partitions quantum circuits across QPUs with quadratic scaling in qubits and lower communication overhead than static baselines.
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Parallel matching-based AMG preconditioners for elliptic equations discretized by IgA
Matching-based AMG preconditioners deliver robust and scalable performance for solving large ill-conditioned systems from IgA discretizations in parallel HPC settings.