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

arXiv: https://arxiv.org/abs/2308.01999 · HTML: https://mlsanity.com/paper/2308.01999/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2023.

## Built on by (most cited first, 19 of 19)

- [Qiskit-Torch-Module: Fast Prototyping of Quantum Neural Networks](https://mlsanity.com/paper/2404.06314/) (2024)
- [QAOA-GPT: Efficient Generation of Adaptive and Regular Quantum Approximate Optimization Algorithm Circuits](https://mlsanity.com/paper/2504.16350/) (2025)
- [Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms](https://mlsanity.com/paper/2510.07317/) (2025)
- [Gated QKAN-FWP: Scalable Quantum-inspired Sequence Learning](https://mlsanity.com/paper/2605.06734/) (2026)
- [Quantum-Enhanced Support Vector Machine for Large-Scale Stellar Classification with GPU Acceleration](https://mlsanity.com/paper/2311.12328/) (2023)
- [Generative Quantum-inspired Kolmogorov-Arnold Eigensolver](https://mlsanity.com/paper/2605.04604/) (2026)
- [VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?](https://mlsanity.com/paper/2607.11985/) (2026)
- [DQAOA-GPT: AI-Accelerated Distributed Quantum Optimization for Combinatorial Problems](https://mlsanity.com/paper/2607.20225/) (2026)
- [Quantum Walks-Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration](https://mlsanity.com/paper/2504.13532/) (2025)
- [QFFN-BERT: An Empirical Study of Depth, Performance, and Data Efficiency in Hybrid Quantum-Classical Transformers](https://mlsanity.com/paper/2507.02364/) (2025)
- [TorchQuantumDistributed](https://mlsanity.com/paper/2511.19291/) (2025)
- [Quantum Approaches to Urban Logistics: From Core QAOA to Clustered Scalability](https://mlsanity.com/paper/2512.10813/) (2025)
- [The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery](https://mlsanity.com/paper/2603.17790/) (2026)
- [Data-Driven Hamiltonian Reduction for Superconducting Qubits via Meta-Learning](https://mlsanity.com/paper/2604.24912/) (2026)
- [Hardware-aware Low-latency Quantum Compilation with Data-driven Lightweight Error Detection for Early Fault-Tolerant Systems](https://mlsanity.com/paper/2606.07666/) (2026)
- [Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder](https://mlsanity.com/paper/2606.27411/) (2026)
- [LC-Implicit-QAOA: Active-Workspace-Capped Exact Objective-and-Gradient Evaluation for Training over Bounded QUBO Light Cones](https://mlsanity.com/paper/2608.05610/) (2026)
- [RASP-QAOA: Resource-Aware Per-Instance Selection for Exact QAOA Simulation](https://mlsanity.com/paper/2608.05646/) (2026)
- [Simulation-Based Quantum System Inference with Neural Posterior Estimation](https://mlsanity.com/paper/2609.34995/) (2026)

## Abstract (arXiv)

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.