Q-GEAR: Improving quantum simulation framework

Abstract

The rapid execution of complex quantum circuit simulations is essential for validating theoretical algorithms, thereby facilitating their successful implementation on quantum hardware. Although mainstream CPU-based platforms for circuit simulations are well established, they tend to be slower. Conversely, the adoption of GPU platforms remains limited because of the necessity for specialized quantum simulation frameworks tailored to different hardware architectures, each requiring distinct implementation and optimization strategies. Therefore, we introduced Q-Gear, a platform-agnostic 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 two orders of magnitude and ten times, respectively, with minimal coding effort. Furthermore, Q-Gear leverages the Cuda-Q configuration to interconnect the memory of GPUs, 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 Shifter image at Perlmutter (NERSC/LBNL), both derived from an NVIDIA public image. These public NERSC containers were optimized for the Slurm job scheduler, allowing approximately 100% utilization of up to 1,024 GPUs. We present various benchmarks for Q-Gear to demonstrate the efficiency of our computational paradigm.