Search Results for « NVIDIA cuQuantum SDK »

We present the NVIDIA cuQuantum SDK [1], 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-based 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 cuQuantum 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.

Noisy intermediate-scale quantum (NISQ) processors are entering an early fault-tolerance regime where full quantum error correction carries prohibitive resource costs, yet lightweight error detection can meaningfully improve algorithmic success rates. Existing compilation and error-detection toolchains treat these concerns in isolation, with no principled way to balance detection overhead against success probability under latency constraints. We present an integrated hardware-aware compilation and data-driven quantum error-detection (QED) framework that jointly optimises qubit mapping, SWAP insertion, and syndrome-schedule placement via a noise-weighted cost function and a learned multi-objective scheduler. Simulation experiments on an HPC cluster using GPU-accelerated density-matrix simulation (NVIDIA cuQuantum SDK) across VQE, phase-estimation, and Grover benchmarks, three noise profiles, and circuit sizes of 6-20 qubits (depths 10-160), show that joint co-design raises algorithmic success probability by up to 68 percent (95 percent CI: 60 percent to 76 percent) over SABRE on an 8-qubit VQE instance with post-selection.

Quantum computer simulators are crucial for the development of quantum computing. In this work, we investigate the suitability and performance impact of GPU and multi-GPU systems on a widely used simulation tool - the state vector simulator Qiskit Aer. In particular, we evaluate the performance of both Qiskit's default Nvidia Thrust backend and the recent Nvidia cuQuantum backend on Nvidia A100 GPUs. We provide a benchmark suite of representative quantum applications for characterization. For simulations with a large number of qubits, the two GPU backends can provide up to 14x speedup over the CPU backend, with Nvidia cuQuantum providing further 1.5-3x speedup over the default Thrust backend. Our evaluation on a single GPU identifies the most important functions in Nvidia Thrust and cuQuantum for different quantum applications and their compute and memory bottlenecks. We also evaluate the gate fusion and cache-blocking optimizations on different quantum applications. Finally, we evaluate large-number qubit quantum applications on multi-GPU and identify data movement between host and GPU as the limiting factor for the performance.

Achieving high-performance computation on quantum systems presents a formidable challenge that necessitates bridging the capabilities between quantum hardware and classical computing resources. This study introduces an innovative distribution-aware Quantum-Classical-Quantum (QCQ) architecture, which integrates cutting-edge quantum software framework works with high-performance classical computing resources to address challenges in quantum simulation for materials and condensed matter physics. At the heart of this architecture is the seamless integration of VQE algorithms running on QPUs for efficient quantum state preparation, Tensor Network states, and QCNNs for classifying quantum states on classical hardware. For benchmarking quantum simulators, the QCQ architecture utilizes the cuQuantum SDK to leverage multi-GPU acceleration, integrated with PennyLane's Lightning plugin, demonstrating up to tenfold increases in computational speed for complex phase transition classification tasks compared to traditional CPU-based methods. This significant acceleration enables models such as the transverse field Ising and XXZ systems to accurately predict phase transitions with a 99.5% accuracy. The architecture's ability to distribute computation between QPUs and classical resources addresses critical bottlenecks in Quantum-HPC, paving the way for scalable quantum simulation. The QCQ framework embodies a synergistic combination of quantum algorithms, machine learning, and Quantum-HPC capabilities, enhancing its potential to provide transformative insights into the behavior of quantum systems across different scales. As quantum hardware continues to improve, this hybrid distribution-aware framework will play a crucial role in realizing the full potential of quantum computing by seamlessly integrating distributed quantum resources with the state-of-the-art classical computing infrastructure.

This article presents a comprehensive study of the performance of quantum circuit simulation using the Matrix Product State (MPS) method within Qiskit Aer. The primary focus is on comparing the computational efficiency of implementations on central processing unit (CPU) and graphics processing unit (GPU) across various simulation platforms. Four key quantum algorithms are experimentally examined: the Variational Quantum Eigensolver (VQE), the Quantum Fourier Transform (QFT), the Quantum Approximate Optimization Algorithm (QAOA), and the Greenberger– Horne–Zeilinger (GHZ) state. For each algorithm, graphs are constructed showing the dependence of execution time on the number of qubits and the type of simulator. A single diagram is also provided to compare all four algorithms using the MPS method in Qiskit Aer. The results indicate that although the MPS simulator in Qiskit Aer is currently implemented only for CPU, it outperforms the traditional statevector simulator for low-entanglement circuits. At the same time, the lack of GPU acceleration limits the scalability of the MPS simulator. Analysis shows that implementing hardware acceleration of SVD operations using NVIDIA's cuQuantum and cuTensorNet libraries can provide a speedup of up to seven times. A comparative analysis of GPU implementations in Qiskit Aer and CUDA-Q is also conducted, including libraries such as cuStateVec, LAPACK, and cuSOLVER. Graphs are created to demonstrate the strengths and weaknesses of the simulators and simulation methods in Qiskit Aer. Notably, the MPS method remains one of the fastest by computation speed on CPU, but is sometimes surpassed by other methods on GPU. Further optimization directions for MPS are proposed, including the development of a hybrid CPU/GPU architecture and the utilization of modern GPU libraries to accelerate critical operations. Keywords: quantum computing, quantum simulation, Qiskit Aer, MPS, statevector, cudaq.

A state vector-based quantum circuit simulation can provide accurate results for the development and validation of quantum computing algorithms, without being affected by noise interference. However, existing quantum circuit simulators have consistently underperformed due to inadequate integration with quantum circuits and high-performance computing architectures. To tackle the challenges in quantum computing, we propose QueenV2, which builds upon the design principles of Queen and elevates performance to a new level. Experimental results on the NVIDIA RTX-4090 demonstrate that QueenV2 achieves up to a 40x improvement in gate performance and a 5x improvement in circuit performance compared to hyQuas. Furthermore, QueenV2 realizes a 137x speedup in gate benchmarks and a 14x speedup in circuit performance relative to NVIDIA cuQuantum, enabled by gate fusion via the IBM Qiskit toolkit. By eliminating reliance on third-party libraries, QueenV2 is positioned to significantly accelerate quantum circuit simulation, thus promoting the development of innovative accelerators and quantum algorithms.

Until high-fidelity quantum computers with a large number of qubits become widely available, classical simulation remains a vital tool for algorithm design, tuning, and validation. We present a simulator for the Quantum Approximate Optimization Algorithm (QAOA). Our simulator is designed with the goal of reducing the computational cost of QAOA parameter optimization and supports both CPU and GPU execution. Our central observation is that the computational cost of both simulating the QAOA state and computing the QAOA objective to be optimized can be reduced by precomputing the diagonal Hamiltonian encoding the problem. We reduce the time for a typical QAOA parameter optimization by eleven times for n = 26 qubits compared to a state-of-the-art GPU quantum circuit simulator based on cuQuantum. Our simulator is available on GitHub: https://github.com/jpmorganchase/QOKit

State-of-the-art quantum circuit simulators have mostly focused on scaling the number of qubits. However, we argue that studying current noisy quantum computers and variational quantum algorithms benefits from high-throughput simulation of intermediate-scale quantum circuits. We present the first implementation and evaluation of a batched quantum simulator on the NEC Vector Engine (VE), a vector processor with massive memory bandwidth that is ideal for memory-intensive state vector simulation. To take advantage of the long-vector architecture of VE, we design a parallelization strategy and memory layout suited for batched state vector simulation. Our evaluation shows that the latest-generation VE Type 30A achieves equal performance as the NVIDIA A100 80 GB model for single-qubit gates, but slightly underperforms A100 for two-qubit gates. Furthermore, our vector-friendly noise gate implementation offers a large speedup compared to that of cuStateVec. When simulating a noisy 16-qubit random circuit with our simulator, VE30 is up to 1.95 × faster than A100.

Rapid advances in quantum computing technology lead to an increasing need for software simulators that enable both algorithm design and the validation of results obtained from quantum hardware. This includes calculations that aim at probing regimes of quantum advantage, where a quantum computer outperforms a classical computer in the same task. High performance computing (HPC) platforms play a crucial role as today’s quantum devices already reach beyond the limits of what powerful workstations can model, but a systematic evaluation of the individual performance of the many offered simulation packages is lacking so far. In this Technical Review, we benchmark several software packages capable of simulating quantum dynamics with a special focus on HPC capabilities. We develop a containerized toolchain for benchmarking a large set of simulation packages on a local HPC cluster using different parallelisation capabilities, and compare the performance and system size-scaling for three paradigmatic quantum computing tasks. Our results can help finding the right package for a given simulation task and lay the foundation for a systematic community effort to benchmark and validate upcoming versions of existing and also newly developed simulation packages.

The state vector-based simulation offers a convenient approach to developing and validating quantum algorithms with noise-free results. However, limited by the absence of cache-aware implementations and unpolished circuit optimizations, the past simulators were severely constrained in performance, leading to stagnation in quantum computing. In this paper, we present an innovative quantum circuit simulation toolkit comprising gate optimization and simulation modules to address these performance challenges. For the performance, scalability, and comprehensive evaluation, we conduct a series of particular circuit benchmarks and strong scaling tests on a DGX-A100 workstation and achieve averaging 9 times speedup compared to state-of-the-art simulators, including QuEST, IBM-Aer, and NVIDIA-cuQuantum. Moreover, the critical performance metric FLOPS increases by up to a factor of 8-fold, and arithmetic intensity experiences a remarkable 96x enhancement. We believe the proposed toolkit paves the way for faster quantum circuit simulations, thereby facilitating the development of novel quantum algorithms.