DgQuEST: Accelerating Large Scale Quantum Circuit Simulation through Hybrid CPU-GPU Memory Hierarchies

Abstract

AbstractWith the advancement of quantum computing, verifying the correctness of the quantum circuits becomes critical while developing new quantum algorithms. Constrained by the obstacles of building practical quantum computers, quantum circuit simulation has become a feasible approach to develop and verify quantum algorithms. Although there are many quantum simulators available, they either achieve low performance on CPUs, or limited simulation scale (e.g., number of qubits) on GPUs due to limited memory capacity. Therefore, we propose dgQuEST, a novel acceleration method that utilizes hybrid CPU-GPU memory hierarchies for large-scale quantum circuit simulation across multiple nodes. dgQuEST adopts efficient memory management and communication schemes to leverage the distributed CPU and GPU memories for accelerating large-scale quantum simulation. Our evaluation demonstrates that dgQuEST achieves an average speedup of 403\(\times \) compared to QuEST on quantum circuit simulation with 32 qubits, and scales to quantum circuit simulation with 35 qubits on two GPU nodes, far beyond the state-of-the-art implementation HyQuas can support.KeywordsQuantum simulationDistributed GPU accelerationMemory and communication optimization