← 研踪 PaperTrailarXiv:2605.25974 · v2
PauLIB: HPC Strategies for Pauli-Arithmetic-Based Quantum Computing Workloads and Simulations
首次提交 2026-05-25 23:48
版本更新 2026-10-26 18:33
雷达观测 2026-10-29 00:36
摘要
As quantum devices become accelerators within classical HPC deployments, the efficiency of the classical partition becomes critical. Workloads such as Hamiltonian manipulation, observable backpropagation, and Pauli-based compilation reduce to bulk arithmetic on Pauli strings and sums. We present HPC strategies for this arithmetic, demonstrated in PauLIB, a header-only C++20 library: a bit-packed binary symplectic representation reducing Pauli multiplication to a bitwise XOR and population count; a sorted, struct-of-arrays layout replacing hash maps and exposing contiguous word arrays to SIMD units and GPU warps; and deterministic OpenMP parallelism with race-free output partitioning. These strategies deliver orders-of-magnitude higher throughput and smaller memory footprint than Qiskit and PauliEngine, plus a speedup from a CUDA outer-product kernel on an H100. Two case studies, g-sim and truncated Pauli propagation, inherit these gains and scale unchanged from a desktop Arm CPU to a 72-core HPC Arm server. PauLIB is open source with C++ and Python interfaces.
作者备注：9 pages. Accepted at SC26 Workshops (Chicago, 2026)
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v2 · 2026-10-26 18:33 · 最新观测版本
PauLIB: HPC Strategies for Pauli-Arithmetic-Based Quantum Computing Workloads and Simulations
As quantum devices become accelerators within classical HPC deployments, the efficiency of the classical partition becomes critical. Workloads such as Hamiltonian manipulation, observable backpropagation, and Pauli-based compilation reduce to bulk arithmetic on Pauli strings and sums. We present HPC strategies for this arithmetic, demonstrated in PauLIB, a header-only C++20 library: a bit-packed binary symplectic representation reducing Pauli multiplication to a bitwise XOR and population count; a sorted, struct-of-arrays layout replacing hash maps and exposing contiguous word arrays to SIMD units and GPU warps; and deterministic OpenMP parallelism with race-free output partitioning. These strategies deliver orders-of-magnitude higher throughput and smaller memory footprint than Qiskit and PauliEngine, plus a speedup from a CUDA outer-product kernel on an H100. Two case studies, g-sim and truncated Pauli propagation, inherit these gains and scale unchanged from a desktop Arm CPU to a 72-core HPC Arm server. PauLIB is open source with C++ and Python interfaces.
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