HQ-Sim: High-performance State Vector Simulation of Quantum Circuits on Heterogeneous HPC Systems
Boyuan Zhang, Bo Fang, Qiang Guan, Ang Li, Dingwen Tao · 2023
Quantum circuit simulations are applied in more and more circumstances as the quantum computing community becomes broader. It helps researchers to evaluate the quantum algorithms and relieve the burden of limited quantum computing resources. However, most of the state-of-the-art quantum simulators utilizes either CPU or GPU to store and calculate the state vector, which results in resources stravation. Morever, the mamximum number of qubits supported by simulator is bounded by the memory, since the memory utilization increases exponentially with the number of qubits. In this study, we leverage Heterogeneous computing to utilize both CPU and GPU to store and update state vectors. We also integrate lossy data compression to reduce memory requirements. Specifically, we develop a heterogeous framework that has a dynamic scheduler to fully utilize the computing resources. We apply lossy compression to chunked state vector to make the maximum number of qubits higher than the regular simulators, the compression also benifits the data movement between CPU and GPU.