Quantum circuit simulators are essential for quantum algorithm development, yet systematic runtime benchmarks comparing major frameworks across CPU and GPU backends are limited. We benchmark three open-source frameworks - Cirq (qsim), CUDA-Q, and Qiskit (Aer) - on statevector simulation of the 2D quantum acoustic wave equation, evolved via Trotterized Hamiltonian dynamics, for problem sizes from 7 to 29 qubits on a single Azure virtual machine. The evaluation spans four categories: CPU runtime versus qubit count, strong scalability on multi-core CPUs at 27 qubits, GPU runtime versus qubit count, and GPU-over-CPU speedup. Relative rankings shift from one category to the next. On CPU, Cirq is fastest up to 23 qubits (2.4x faster than Qiskit at 21 qubits), but Qiskit overtakes it at larger sizes and higher thread counts, sustaining 80% parallel efficiency at 16 threads. On GPU, CUDA-Q leads between 13 and 21 qubits, while Cirq becomes the fastest GPU backend at large problem sizes, achieving 1.5x and 2.3x speedups over CUDA-Q and Qiskit at 29 qubits. GPU-over-CPU speedup ratios should be interpreted alongside absolute GPU runtimes; CUDA-Q's 1500x at 21 qubits is largely driven by the limited scaling of its CPU backend (qpp), and the absolute GPU runtimes show that the three frameworks' GPU backends differ by less than these ratios alone suggest. These results indicate that no single framework dominates across configurations, and that backend selection for statevector simulation should be guided by both target hardware and problem size.
Quantum circuit simulators are essential for quantum algorithm development, yet systematic runtime benchmarks comparing major frameworks across CPU and GPU backends are limited. We benchmark three open-source frameworks - Cirq (qsim), CUDA-Q, and Qiskit (Aer) - on statevector simulation of the 2D quantum acoustic wave equation, evolved via Trotterized Hamiltonian dynamics, for problem sizes from 7 to 29 qubits on a single Azure virtual machine. The evaluation spans four categories: CPU runtime versus qubit count, strong scalability on multi-core CPUs at 27 qubits, GPU runtime versus qubit count, and GPU-over-CPU speedup. Relative rankings shift from one category to the next. On CPU, Cirq is fastest up to 23 qubits (2.4x faster than Qiskit at 21 qubits), but Qiskit overtakes it at larger sizes and higher thread counts, sustaining 80% parallel efficiency at 16 threads. On GPU, CUDA-Q leads between 13 and 21 qubits, while Cirq becomes the fastest GPU backend at large problem sizes, achieving 1.5x and 2.3x speedups over CUDA-Q and Qiskit at 29 qubits. GPU-over-CPU speedup ratios should be interpreted alongside absolute GPU runtimes; CUDA-Q's 1500x at 21 qubits is largely driven by the limited scaling of its CPU backend (qpp), and the absolute GPU runtimes show that the three frameworks' GPU backends differ by less than these ratios alone suggest. These results indicate that no single framework dominates across configurations, and that backend selection for statevector simulation should be guided by both target hardware and problem size.
The rapid expansion of quantum computing research has created a growing need for effective and scalable tools to simulate quantum algorithms. In this study, we present a performance analysis of several quantum circuit simulators by using the Deutsch-Jozsa (DJ) and Quantum Fourier Transform (QFT). A gate-based QASM implementation serves as the baseline and is converted to code compatible with each simulator. Our evaluation covers multi-CPU simulators like Qiskit, Qsim-Cirq, and QuEST, as well as their GPU-accelerated variants along with CUDA-Q, for qubit sizes ranging from 21 to 35. By profiling these algorithms, we compare key performance indicators and reveal notable efficiency differences between platforms. In particular, the multi-node version of QuEST shows strong CPU utilization, high speedup, and efficient memory scalability. whereas GPUbased implementations provide clear performance advantages at higher qubit counts. These findings offer practical guidance for choosing simulation platforms according to problem size and available computing resources, thereby supporting more efficient quantum-algorithm workflows.
Quantum circuit simulation at scale demands high-performance computing (HPC) frameworks capable of distributing exponentially growing state vectors and tensor networks across heterogeneous compute clusters. As quantum algorithm development accelerates and circuits approach 50-100 qubits in practical workloads, simulation bottlenecks shift from single-node memory constraints to inter-node communication overhead, GPU memory bandwidth, and framework-level scheduling inefficiencies. This paper proposes the HPC Quantum Simulation Framework Benchmark (HQSFB), a systematic evaluation of five leading quantum simulation frameworks -- Qiskit Aer, Cirq on GCP, cuQuantum (NVIDIA), QuEST, and Intel Quantum Simulator (IQS) -- across six HPC deployment configurations spanning single-GPU workstations to 512-GPU supercomputer partitions. HQSFB introduces the Framework Efficiency Score (FES) combining simulation throughput, GPU utilisation, inter-node communication overhead, and memory scaling efficiency. Key results: cuQuantum achieves the highest FES (0.924) with 94.2% GPU utilisation and 42x speedup over CPU baseline at 512 GPUs; QuEST achieves the best memory scaling efficiency (0.962) for distributed statevector simulation; Qiskit Aer provides the broadest circuit compatibility with FES = 0.842. The framework provides HPC configuration guidance and an open benchmark suite for quantum simulation infrastructure planning.
Classical simulation of quantum circuits remains indispensable for algorithm development, hardware validation, and error analysis in the noisy intermediate-scale quantum (NISQ) era. However, state-vector simulation faces exponential memory scaling, with an n-qubit system requiring O(2^n) complex amplitudes, and existing simulators often lack the flexibility to exploit heterogeneous computing resources at runtime. This paper presents a GPU-accelerated quantum circuit simulation framework that introduces three contributions: (1) an empirical backend selection algorithm that benchmarks CuPy, PyTorch-CUDA, and NumPy-CPU backends at runtime and selects the optimal execution path based on measured throughput; (2) a directed acyclic graph (DAG) based gate fusion engine that reduces circuit depth through automated identification of fusible gate sequences, coupled with adaptive precision switching between complex64 and complex128 representations; and (3) a memory-aware fallback mechanism that monitors GPU memory consumption and gracefully degrades to CPU execution when resources are exhausted. The framework integrates with Qiskit, Cirq, PennyLane, and Amazon Braket through a unified adapter layer. Benchmarks on an NVIDIA A100-SXM4 (40 GiB) GPU demonstrate speedups of 64x to 146x over NumPy CPU execution for state-vector simulation of circuits with 20 to 28 qubits, with speedups exceeding 5x from 16 qubits onward. Hardware validation on an IBM quantum processing unit (QPU) confirms Bell state fidelity of 0.939, a five-qubit Greenberger-Horne-Zeilinger (GHZ) state fidelity of 0.853, and circuit depth reduction from 42 to 14 gates through the fusion pipeline. The system is designed for portability across NVIDIA consumer and data-center GPUs, requiring no vendor-specific compilation steps.
The increasing complexity of quantum algorithms and the limitations of current Noisy Intermediate-Scale Quantum (NISQ) hardware underscore the importance of efficient classical simulators. To support informed decision-making by users of quantum circuit simulators, we benchmark seven statevector-based quantum circuit simulators (Qiskit, Qulacs, Qibo, Qsimov, Cirq, Pennylane and the Intel Quantum Simulator (IQS)) on a multicore node of the Lusitania high-performance computing (HPC) system. We evaluate their performance in terms of execution time, memory usage and core scalability using Grover’s algorithm, the quantum Fourier transform (QFT), and quantum volume (QV) circuits, across qubit counts ranging from 3 to 30. Our results reveal that Qulacs offers the best performance for circuits below 22 qubits, while Qiskit becomes the fastest for larger and more complex circuits. Qiskit and Qulacs achieve the most efficient parallel performance across multiple cores, while others display limited scaling benefits. IQS shows the lowest memory consumption in QFT and QV benchmarks for systems under 24 qubits; however, it suffers from higher execution times, particularly for Grover’s algorithm. The experimental implementation of Qsimov consistently underperforms in both runtime and scalability; for this reason, it is employed as a baseline in our measurements, serving to highlight the importance of performance optimizations in statevector-based quantum circuit simulators. Previous findings provide a comprehensive performance landscape to guide researchers in selecting appropriate simulators for both standard and large-scale quantum workloads on HPC infrastructures.
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.
Practitioners increasingly rely on hosted simulation environments, but their performance characteristics remain poorly documented. We present a systematic benchmarking study of GPU-accelerated approximate quantum simulation across two widely used methods: matrix product states (MPS) and Pauli path simulation (PPS), comparing BlueQubit (a hosted tool that handles hardware provisioning, simulator configuration, and job orchestration) against AWS Braket, Quantum Rings, Qiskit pauli-prop, and PauliPropagation (written in Julia). For MPS, we find that GPU runtime yields sub-quadratic scaling with bond dimension, with a growing advantage over CPU at increasing scale. For Pauli path simulation on IBM's 127-qubit kicked Ising benchmark, GPUs deliver up to ∼1,700× speedup at fine truncation thresholds (δ=2.5×10−5, 27.6M Pauli terms), and are the only backends that reach accuracy regimes below δ=10−5, which remained inaccessible to the commodity CPU-based implementations and self-contained SDKs evaluated here. We also provide a reproducible characterization of these simulators across regimes, including tradeoffs that isolated evaluations do not show. All benchmarking code and configurations are in a public GitHub repository.