Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

IEEE International Conference on Quantum Computing and Engineering (QCE)

CUAOA: A Novel CUDA-Accelerated Simulation Framework for the QAOA

Jonas Stein, Jonas Blenninger, David Bucher, Peter J. Eder, Elif Çetiner, Maximilian Zorn, Claudia Linnhoff–Popien

Published Sep 15, 2024• 2 citations
Publisher

Abstract

The Quantum Approximate Optimization Algorithm (QAOA) is a prominent quantum algorithm designed to find approximate solutions to combinatorial optimization problems, which are challenging for classical computers. In the current era, where quantum hardware is constrained by noise and limited qubit availability, simulating the QAOA remains essential for research. However, existing state-of-the-art simulation frame-works suffer from long execution times or lack comprehensive functionality, usability, and versatility, often requiring users to implement essential features themselves. Additionally, these frameworks are primarily restricted to Python, limiting their use in safer and faster languages like Rust, which offer, e.g., advanced parallelization capabilities. In this paper, we develop a GPU accelerated QAOA simulation framework utilizing the NVIDIA CUDA toolkit. This framework offers a complete interface for QAOA simulations, enabling the calculation of (exact) expectation values, direct access to the statevector, fast sampling, and high-performance optimization methods using an advanced state-of-the-art gradient calculation technique. The framework is designed for use in Python and Rust, providing flexibility for integration into a wide range of applications, including those requiring fast algorithm implementations leveraging QAOA at its core. The new framework's performance is rigorously benchmarked on the MaxCut problem and compared against the current state-of-the-art general-purpose quantum circuit simulation frameworks Qiskit and Pennylane as well as the specialized QAOA simulation tool QOKit. Our evaluation shows that our approach outperforms the existing state-of-the-art solutions in terms of runtime up to multiple orders of magnitude. Our implementation is publicly available at https://github.com/JFLXB/cuaoa and Zenodo [1].

DOI
10.1109/qce60285.2024.10292

Related papers

CUAOA: A Novel CUDA-Accelerated Simulation Framework for the QAOA

Jonas Stein, Jonas Stein, Bucher, David +4 more

CUAOA is a GPU accelerated QAOA simulation framework utilizing the NVIDIA CUDA toolkit. This framework offers a complete interface for QAOA simulations, enabling the calculation of (exact) expectation values, direct access to the statevector, fast sampling, and high-performance optimization methods using an advanced state-of-the-art gradient calculation technique. The framework is designed for use in Python and Rust, providing flexibility for integration into a wide range of applications, including those requiring fast algorithm implementations leveraging QAOA at its core.

arXiv:2407.13012•Jul 17, 2024•0 citations

Fast Simulation of High-Depth QAOA Circuits

Danylo Lykov, Ruslan Shaydulin, Yue Sun +2 more

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

arXiv:2309.04841•International Conference for High Performance Computing, Networking, Storage and Analysis (SC)•Nov 10, 2023•27 citations

Assessing the Efficacy of Hybrid Quantum-Classical Computing for Combinatorial Optimization: A Comparative Analysis of QAOA and Classical Solvers

Ayan Khan

The transition from theoretical quantum computing to practical real-world applications heavily relies onhybrid quantum-classical architectures designed for NoisyIntermediate-Scale Quantum (NISQ) devices. Among these approaches, the Quantum Approximate Optimization Algorithm(QAOA) is a prominent strategy for addressing complex combinatorial optimization challenges, such as the NP-hard MaxCut problem. Instead of performing the entire computationon a quantum processor, QAOA utilizes a quantum circuit toevaluate a parameterized cost function, whereas a classical optimizer refines the parameters iteratively. This study presentsa rigorous empirical evaluation comparing the performanceof QAOA against established classical solvers on small-scaleMax-Cut instances. All experiments were implemented andexecuted using Python (Qiskit Aer) on a state-vector simulator.Using Erdős–Rényi random graphs and regular graphs with4 to 20 nodes, we benchmark QAOA at depths p = 2 andp = 3 against simulated annealing, the Goemans–WilliamsonSDP-based algorithm, and a linear-time greedy heuristic. Ourexperimental results demonstrate that low-depth QAOA implementations exhibit severe performance degradation, prohibitive computational overhead (O(2^n) scaling), and low convergencerates compared to mature classical algorithms. At 20 nodes,QAOA (p = 2) achieves only a 0.467 approximation ratio versus0.856 for Goemans–Williamson, with a 210× runtime slowdownover greedy heuristics

•Jun 3, 2026•0 citations

Towards Optimizations of Quantum Circuit Simulation for Solving Max-Cut Problems with QAOA

Yu-Cheng Lin, Chuan-Chi Wang, Chia-Heng Tu +1 more

Quantum approximate optimization algorithm (QAOA) is one of the popular quantum algorithms that are used to solve combinatorial optimization problems via approximations. QAOA is able to be evaluated on both physical and virtual quantum computers simulated by classical computers, with virtual ones being favored for their noise-free feature and availability. Nevertheless, performing QAOA on virtual quantum computers suffers from a slow simulation speed for solving combinatorial optimization problems which require large-scale quantum circuit simulation (QCS). In this paper, we propose techniques to accelerate QCS for QAOA using mathematical optimizations to compress quantum operations, incorporating efficient bitwise operations to further lower the computational complexity, and leveraging different levels of parallelisms from modern multi-core processors, with a study case to show the effectiveness on solving max-cut problems. The experimental results reveal substantial performance improvements, surpassing a state-of-the-art simulator, QuEST, by a factor of 17 on a virtual quantum computer running on a 16-core, 32-thread AMD Ryzen 9 5950X processor. We believe that this work opens up new possibilities for accelerating various QAOA applications.

arXiv:2312.03019•ACM Symposium on Applied Computing•Apr 8, 2024•10 citations

Q-GEAR: Improving quantum simulation framework

Ziqing Guo, Ziwen Pan, J. T. Balewski

Fast execution of complex quantum circuit simulations are crucial for verification of theoretical algorithms paving the way for their successful execution on the quantum hardware. However, the main stream CPU-based platforms for circuit simulation are well-established but slower. Despite this, adoption of GPU platforms remains limited because different hardware architectures require specialized quantum simulation frameworks, each with distinct implementations and optimization strategies. Therefore, we introduce Q-Gear, a software framework that transforms Qiskit quantum circuits into Cuda-Q kernels. By leveraging Cuda-Q seamless execution on GPUs, Q-Gear accelerates both CPU and GPU based simulations by respectively two orders of magnitude and ten times with minimal coding effort. Furthermore, Q-Gear leverages Cuda-Q configuration to interconnect GPUs memory allowing the execution of much larger circuits, beyond the memory limit set by a single GPU or CPU node. Additionally, we created and deployed a Podman container and a Shifter image at Perlmutter (NERSC/LBNL), both derived from NVIDIA public image. These public NERSC containers were optimized for the Slurm job scheduler allowing for close to 100% GPU utilization. We present various benchmarks of the Q-Gear to prove the efficiency of our computation paradigm.

arXiv:2504.03967•Apr 4, 2025•0 citations

Q-GEAR: Improving quantum simulation framework

Ziqing Guo, J. T. Balewski, Ziwen Pan

The rapid execution of complex quantum circuit simulations is essential for validating theoretical algorithms, thereby facilitating their successful implementation on quantum hardware. Although mainstream CPU-based platforms for circuit simulations are well established, they tend to be slower. Conversely, the adoption of GPU platforms remains limited because of the necessity for specialized quantum simulation frameworks tailored to different hardware architectures, each requiring distinct implementation and optimization strategies. Therefore, we introduced Q-Gear, a platform-agnostic framework that transforms Qiskit quantum circuits into Cuda-Q kernels. By leveraging Cuda-Q seamless execution on GPUs, Q-Gear accelerates both CPU- and GPU-based simulations by two orders of magnitude and ten times, respectively, with minimal coding effort. Furthermore, Q-Gear leverages the Cuda-Q configuration to interconnect the memory of GPUs, allowing the execution of much larger circuits beyond the memory limit set by a single GPU or CPU node. Additionally, we created and deployed a Podman container and Shifter image at Perlmutter (NERSC/LBNL), both derived from an NVIDIA public image. These public NERSC containers were optimized for the Slurm job scheduler, allowing approximately 100% utilization of up to 1,024 GPUs. We present various benchmarks for Q-Gear to demonstrate the efficiency of our computational paradigm.

Proceedings of the International Conference on Parallel Processing•Sep 8, 2025•0 citations
The Quantum Approximate Optimization Algorithm (QAOA) is a prominent quantum algorithm designed to find approximate solutions to combinatorial optimization problems, which are challenging for classical computers. In the current era, where quantum hardware is constrained by noise and limited qubit availability, simulating the QAOA remains essential for research. However, existing state-of-the-art simulation frame-works suffer from long execution times or lack comprehensive functionality, usability, and versatility, often requiring users to implement essential features themselves. Additionally, these frameworks are primarily restricted to Python, limiting their use in safer and faster languages like Rust, which offer, e.g., advanced parallelization capabilities. In this paper, we develop a GPU accelerated QAOA simulation framework utilizing the NVIDIA CUDA toolkit. This framework offers a complete interface for QAOA simulations, enabling the calculation of (exact) expectation values, direct access to the statevector, fast sampling, and high-performance optimization methods using an advanced state-of-the-art gradient calculation technique. The framework is designed for use in Python and Rust, providing flexibility for integration into a wide range of applications, including those requiring fast algorithm implementations leveraging QAOA at its core. The new framework's performance is rigorously benchmarked on the MaxCut problem and compared against the current state-of-the-art general-purpose quantum circuit simulation frameworks Qiskit and Pennylane as well as the specialized QAOA simulation tool QOKit. Our evaluation shows that our approach outperforms the existing state-of-the-art solutions in terms of runtime up to multiple orders of magnitude. Our implementation is publicly available at https://github.com/JFLXB/cuaoa and Zenodo [1].
CUAOA is a GPU accelerated QAOA simulation framework utilizing the NVIDIA CUDA toolkit. This framework offers a complete interface for QAOA simulations, enabling the calculation of (exact) expectation values, direct access to the statevector, fast sampling, and high-performance optimization methods using an advanced state-of-the-art gradient calculation technique. The framework is designed for use in Python and Rust, providing flexibility for integration into a wide range of applications, including those requiring fast algorithm implementations leveraging QAOA at its core.
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
The transition from theoretical quantum computing to practical real-world applications heavily relies onhybrid quantum-classical architectures designed for NoisyIntermediate-Scale Quantum (NISQ) devices. Among these approaches, the Quantum Approximate Optimization Algorithm(QAOA) is a prominent strategy for addressing complex combinatorial optimization challenges, such as the NP-hard MaxCut problem. Instead of performing the entire computationon a quantum processor, QAOA utilizes a quantum circuit toevaluate a parameterized cost function, whereas a classical optimizer refines the parameters iteratively. This study presentsa rigorous empirical evaluation comparing the performanceof QAOA against established classical solvers on small-scaleMax-Cut instances. All experiments were implemented andexecuted using Python (Qiskit Aer) on a state-vector simulator.Using Erdős–Rényi random graphs and regular graphs with4 to 20 nodes, we benchmark QAOA at depths p = 2 andp = 3 against simulated annealing, the Goemans–WilliamsonSDP-based algorithm, and a linear-time greedy heuristic. Ourexperimental results demonstrate that low-depth QAOA implementations exhibit severe performance degradation, prohibitive computational overhead (O(2^n) scaling), and low convergencerates compared to mature classical algorithms. At 20 nodes,QAOA (p = 2) achieves only a 0.467 approximation ratio versus0.856 for Goemans–Williamson, with a 210× runtime slowdownover greedy heuristics
Quantum approximate optimization algorithm (QAOA) is one of the popular quantum algorithms that are used to solve combinatorial optimization problems via approximations. QAOA is able to be evaluated on both physical and virtual quantum computers simulated by classical computers, with virtual ones being favored for their noise-free feature and availability. Nevertheless, performing QAOA on virtual quantum computers suffers from a slow simulation speed for solving combinatorial optimization problems which require large-scale quantum circuit simulation (QCS). In this paper, we propose techniques to accelerate QCS for QAOA using mathematical optimizations to compress quantum operations, incorporating efficient bitwise operations to further lower the computational complexity, and leveraging different levels of parallelisms from modern multi-core processors, with a study case to show the effectiveness on solving max-cut problems. The experimental results reveal substantial performance improvements, surpassing a state-of-the-art simulator, QuEST, by a factor of 17 on a virtual quantum computer running on a 16-core, 32-thread AMD Ryzen 9 5950X processor. We believe that this work opens up new possibilities for accelerating various QAOA applications.
Fast execution of complex quantum circuit simulations are crucial for verification of theoretical algorithms paving the way for their successful execution on the quantum hardware. However, the main stream CPU-based platforms for circuit simulation are well-established but slower. Despite this, adoption of GPU platforms remains limited because different hardware architectures require specialized quantum simulation frameworks, each with distinct implementations and optimization strategies. Therefore, we introduce Q-Gear, a software framework that transforms Qiskit quantum circuits into Cuda-Q kernels. By leveraging Cuda-Q seamless execution on GPUs, Q-Gear accelerates both CPU and GPU based simulations by respectively two orders of magnitude and ten times with minimal coding effort. Furthermore, Q-Gear leverages Cuda-Q configuration to interconnect GPUs memory allowing the execution of much larger circuits, beyond the memory limit set by a single GPU or CPU node. Additionally, we created and deployed a Podman container and a Shifter image at Perlmutter (NERSC/LBNL), both derived from NVIDIA public image. These public NERSC containers were optimized for the Slurm job scheduler allowing for close to 100% GPU utilization. We present various benchmarks of the Q-Gear to prove the efficiency of our computation paradigm.
The rapid execution of complex quantum circuit simulations is essential for validating theoretical algorithms, thereby facilitating their successful implementation on quantum hardware. Although mainstream CPU-based platforms for circuit simulations are well established, they tend to be slower. Conversely, the adoption of GPU platforms remains limited because of the necessity for specialized quantum simulation frameworks tailored to different hardware architectures, each requiring distinct implementation and optimization strategies. Therefore, we introduced Q-Gear, a platform-agnostic framework that transforms Qiskit quantum circuits into Cuda-Q kernels. By leveraging Cuda-Q seamless execution on GPUs, Q-Gear accelerates both CPU- and GPU-based simulations by two orders of magnitude and ten times, respectively, with minimal coding effort. Furthermore, Q-Gear leverages the Cuda-Q configuration to interconnect the memory of GPUs, allowing the execution of much larger circuits beyond the memory limit set by a single GPU or CPU node. Additionally, we created and deployed a Podman container and Shifter image at Perlmutter (NERSC/LBNL), both derived from an NVIDIA public image. These public NERSC containers were optimized for the Slurm job scheduler, allowing approximately 100% utilization of up to 1,024 GPUs. We present various benchmarks for Q-Gear to demonstrate the efficiency of our computational paradigm.