Q-GEAR: Improving quantum simulation framework

• URL: http://arxiv.org/abs/2504.03967v1
• Date: Fri, 04 Apr 2025 22:17:51 GMT
• Title: Q-GEAR: Improving quantum simulation framework
• Authors: Ziqing Guo, Ziwen Pan, Jan Balewski,
• Abstract summary: We introduce Q-Gear, a software framework that transforms Qiskit quantum circuits into Cuda-Q kernels.<n>Q-Gear accelerates both CPU and GPU based simulations by respectively two orders of magnitude and ten times with minimal coding effort.
• Score: 0.28402080392117757
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fast execution of complex quantum circuit simulations are crucial for verification of theoretical algorithms paving the way for their successful execution on the quantum hardware. However, the main stream CPU-based platforms for circuit simulation are well-established but slower. Despite this, adoption of GPU platforms remains limited because different hardware architectures require specialized quantum simulation frameworks, each with distinct implementations and optimization strategies. Therefore, we introduce Q-Gear, a software framework that transforms Qiskit quantum circuits into Cuda-Q kernels. By leveraging Cuda-Q seamless execution on GPUs, Q-Gear accelerates both CPU and GPU based simulations by respectively two orders of magnitude and ten times with minimal coding effort. Furthermore, Q-Gear leverages Cuda-Q configuration to interconnect GPUs memory allowing the execution of much larger circuits, beyond the memory limit set by a single GPU or CPU node. Additionally, we created and deployed a Podman container and a Shifter image at Perlmutter (NERSC/LBNL), both derived from NVIDIA public image. These public NERSC containers were optimized for the Slurm job scheduler allowing for close to 100% GPU utilization. We present various benchmarks of the Q-Gear to prove the efficiency of our computation paradigm.

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