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arXiv:2605.25974

PauLIB: A High-Performance Library for Processing Pauli Strings

Florian Krötz

quant-phcs.ETSubmitted May 25, 2026• Announced May 26, 2026• 0 citations
v1 · May 25, 2026v2 · Oct 26, 2026
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Abstract

Processing large Pauli sums is a significant bottleneck in quantum chemistry, Pauli propagation, and Pauli-based compilation. Existing frameworks often suffer from Python interpreter overhead or utilize hash-map data structures that hinder SIMD vectorization and complicate multi-threaded merging. We present PauLIB, a header-only C++20 library designed to eliminate these bottlenecks through three key architectural choices. A bit-packed binary symplectic representation that encodes each qubit in two bits, reducing Pauli multiplication to a bitwise XOR and a population count; a sorted array layout that replaces hash maps to enable branch-predictable SIMD bulk operations; and a struct-of-arrays (SoA) memory layout that exposes contiguous word arrays for explicit SIMD vectorization. Benchmarks at 500 qubits show that single Pauli string multiplication runs at 25ns per operation-14 times faster than PauliEngine and 660 times faster than Qiskit-flat across all pair counts tested. Hamiltonian outer-product multiplication is approximately 10 times faster than PauliEngine and 45 times faster than Qiskit at all tested sizes. Greedy commutation grouping, the dominant preprocessing cost in variational algorithms, achieves up to 21,000 times speedup over PennyLane, driven by the compact bit-packed representation. The compact layout reduces the memory footprint of a one-million-term Hamiltonian at 500 qubits from 1,036MB (Qiskit) to 142MB, a 7.3 times reduction that directly enables larger problem sizes within a fixed memory budget. PauLIB is open source and provides C++ and Python interfaces.

Comments
This work has been submitted to the IEEE QCE

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Pauli strings are a fundamental computational primitive in hybrid quantum-classical algorithms. However, classical computation of Pauli strings suffers from exponential complexity and quickly becomes a performance bottleneck as the number of qubits increases. To address this challenge, this paper proposes the Pauli Composer Accelerator (PACOX), the first dedicated FPGA-based accelerator for Pauli string computation. PACOX employs a compact binary encoding with XOR-based index permutation and phase accumulation. Based on this formulation, we design a parallel and pipelined processing element (PE) cluster architecture that efficiently exploits data-level parallelism on FPGA. Experimental results on a Xilinx ZCU102 FPGA show that PACOX operates at 250 MHz with a dynamic power consumption of 0.33 W, using 8,052 LUTs, 10,934 FFs, and 324 BRAMs. For Pauli strings of up to 19 qubits, PACOX consistently outperforms state-of-the-art CPU-based methods in terms of execution speed, while also requiring significantly less memory and achieving a much lower power-delay product. These results demonstrate that PACOX delivers high computational speed with superior energy efficiency for Pauli-based workloads in hybrid quantum-classical systems.
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Abstract Large-scale classical simulation of quantum computers is crucial for benchmarking quantum algorithms, establishing boundaries of quantum advantage and exploring heuristic quantum algorithms. We present a full-state vector simulation algorithm and software implementation designed to perform HPC simulation of layers of rotations around a string of Pauli operators. We demonstrate robust scalability of the simulation method on large distributed CPU and GPU systems. Our distributed computation harnessed up to 16384 CPU cores and 512 NVIDIA H100 GPUs, using 32 TB of memory. The simulator significantly outperforms other high-performance libraries, showing a typical speedup of 10-100 for large-scale multi-GPU workloads. As a first application of our approach, we report a numerical experiment aimed at simulating exactly Hamiltonian dynamics of up to 40 qubits to investigate the Trotter error for a quantum chemistry problem. Bounding the Trotter error is important for evaluating the cost of quantum algorithms for chemistry, and rigorous bounds are often conservative, as our simulations confirm. Our software, specifically designed for quantum time evolution applications, is also well equipped to manage circuits that utilize standard gate sets.