cuQuantum SDK: A High-Performance Library for Accelerating Quantum Science

TL;DR

The paper introduces the NVIDIA cuQuantum SDK, a high-performance library that accelerates quantum circuit simulations on GPUs, supporting various simulation methods and enabling scalable, distributed quantum computing research.

Contribution

It provides a comprehensive, optimized library of primitives for GPU-accelerated quantum simulation, supporting state vector and tensor network methods with scalable, distributed capabilities.

Findings

Significant acceleration of quantum simulations on NVIDIA GPUs.

Support for both state vector and tensor network simulation methods.

Ease of transitioning to distributed GPU platforms for larger simulations.

Abstract

We present the NVIDIA cuQuantum SDK, a state-of-the-art library of composable primitives for GPU-accelerated quantum circuit simulations. As the size of quantum devices continues to increase, making their classical simulation progressively more difficult, the availability of fast and scalable quantum circuit simulators becomes vital for quantum algorithm developers, as well as quantum hardware engineers focused on the validation and optimization of quantum devices. The cuQuantum SDK was created to accelerate and scale up quantum circuit simulators developed by the quantum information science community by enabling them to utilize efficient scalable software building blocks optimized for NVIDIA GPU platforms. The functional building blocks provided cover the needs of both state vector- and tensor network- based simulators, including approximate tensor network simulation methods based on matrix product state, projected entangled pair state, and other factorized tensor representations. By leveraging the enormous computing power of the latest NVIDIA GPU architectures, quantum circuit simulators that have adopted the cuQuantum SDK demonstrate significant acceleration, compared to CPU-only execution, for both the state vector and tensor network simulation methods. Furthermore, by utilizing the parallel primitives available in the cuQuantum SDK, one can easily transition to distributed GPU-accelerated platforms, including those furnished by cloud service providers and high-performance computing systems deployed by supercomputing centers, extending the scale of possible quantum circuit simulations. The rich capabilities provided by the SDK are conveniently made available via both Python and C application programming interfaces, where the former is directly targeting a broad Python quantum community and the latter allows tight integration with simulators written in any programming language.