Cobble is a domain-specific language for quantum block encodings that compiles high-level matrix expressions to optimized circuits using analyses and quantum singular value transformation, achieving 2.6x-25.4x speedups over unoptimized baselines on benchmarks.
ACM Transactions on Mathematical Software48(1), 2:1–2:33 (2022)
5 Pith papers cite this work, alongside 76 external citations. Polarity classification is still indexing.
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KerneLDI accelerates exchange-correlation integration in Kohn-Sham DFT by up to 10x through block-structured matrix multiplication that exploits spatial locality on GPUs while preserving accuracy.
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An optical LPU encodes selected sparse linear systems into laser phases to achieve lower time-to-solution than GPU-based iterative solvers for structured problems.
Review chapter summarizing advances in parallel sparse direct solvers along communication reduction and data-sparse compression axes.
citing papers explorer
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Cobble: Compiling Block Encodings for Quantum Computational Linear Algebra
Cobble is a domain-specific language for quantum block encodings that compiles high-level matrix expressions to optimized circuits using analyses and quantum singular value transformation, achieving 2.6x-25.4x speedups over unoptimized baselines on benchmarks.
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Accelerating Locality-Driven Integration in Quantum Chemistry with Block-Structured Matrix Multiplication
KerneLDI accelerates exchange-correlation integration in Kohn-Sham DFT by up to 10x through block-structured matrix multiplication that exploits spatial locality on GPUs while preserving accuracy.
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Randomized Sketching is Robust to Low-Precision Rounding on GPUs
FP16 SparseStack on GPUs shows embedding quality insensitive to rounding method, with sketch distribution as the primary accuracy driver across tested inputs.
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Accelerating Sparse Linear Solvers with an Optical Laser Processing Unit
An optical LPU encodes selected sparse linear systems into laser phases to achieve lower time-to-solution than GPU-based iterative solvers for structured problems.
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Parallel Sparse and Data-Sparse Factorization-based Linear Solvers
Review chapter summarizing advances in parallel sparse direct solvers along communication reduction and data-sparse compression axes.