Quantum computer simulators play a critical role in supporting the development and validation of quantum algorithms and hardware. This study focuses on porting Google’s qsim, a quantum computer simulator, to AMD Graphics Processing Units (GPUs). We leverage the existing qsim CUDA backend and harness the HIPIFY tool to provide a qsim HIP backend tailored for AMD GPUs. Our performance analysis centers on evaluating the HIP backend’s capabilities, executed on a computing node equipped with the AMD MI250X GPU and the AMD EPYC Trento CPU. We use the Random Quantum Circuit (RQC) sampling benchmark, employing a circuit featuring 30 qubits. The qsim HIP backend on AMD GPU outperforms the CPU version by a remarkable margin, achieving seven to nine times faster speeds. Our investigation also compares qsim’s performance on the Nvidia A100 and AMD MI250X GPUs. The Nvidia A100 consistently outperforms the AMD MI250x counterpart, and this performance gap further widens with optimal gate fusion configurations. For instance, a two-gate fusion configuration exhibits a 5% difference, whereas a four-gate fusion setup reveals a large 44% performance gap. Our work highlights the substantial performance advantage of GPU-based quantum simulation over traditional CPU approaches. Despite a performance lag compared to the qsim CUDA backend, the AMD HIP qsim backend emerges as a competitive alternative poised for further optimization.
Quantum computer simulators play a critical role in supporting the development and validation of quantum algorithms and hardware. This study focuses on porting Google’s qsim, a quantum computer simulator, to AMD Graphics Processing Units (GPUs). We leverage the existing qsim CUDA backend and harness the HIPIFY tool to provide a qsim HIP backend tailored for AMD GPUs. Our performance analysis centers on evaluating the HIP backend’s capabilities, executed on a computing node equipped with the AMD MI250X GPU and the AMD EPYC Trento CPU. We use the Random Quantum Circuit (RQC) sampling benchmark, employing a circuit featuring 30 qubits. The qsim HIP backend on AMD GPU outperforms the CPU version by a remarkable margin, achieving seven to nine times faster speeds. Our investigation also compares qsim’s performance on the Nvidia A100 and AMD MI250X GPUs. The Nvidia A100 consistently outperforms the AMD MI250x counterpart, and this performance gap further widens with optimal gate fusion configurations. For instance, a two-gate fusion configuration exhibits a 5% difference, whereas a four-gate fusion setup reveals a large 44% performance gap. Our work highlights the substantial performance advantage of GPU-based quantum simulation over traditional CPU approaches. Despite a performance lag compared to the qsim CUDA backend, the AMD HIP qsim backend emerges as a competitive alternative poised for further optimization.
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.
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.
Quantum computer simulators are crucial for the development of quantum computing. This work investigates GPU and multi-GPU systems' suitability and performance impact 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 14× speedup over the CPU backend, with Nvidia cuQuantum providing a further 1.5–3× 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.
In recent years, quantum computing has undergone significant developments and has established its supremacy in many application domains. While quantum hardware is accessible to the public through the cloud environment, a robust and efficient quantum circuit simulator is necessary to investigate the constraints and foster quantum computing development, such as quantum algorithm development and quantum device architecture exploration. In this paper, we observe that most of the publicly available quantum circuit simulators (e.g., QISKit from IBM, QDK from Microsoft, and Qsim-Cirq from Google) suffer from slow simulation and poor scalability when the number of qubits increases. To this end, we systematically investigate the deficiencies in quantum circuit simulation (QCS) and propose Q-GPU, a framework that leverages GPUs with comprehensive optimizations to allow efficient and scalable QCS. Specifically, Q-GPU features i) proactive state amplitude transfer, ii) zero state amplitude pruning, iii) delayed qubit involvement, and iv) lossless nonzero state amplitude compression. Experimental results across nine representative quantum circuits indicate that Q-GPU significantly reduces the execution time of the state-of-the-art GPU-based QCS by 71.89% (3.55× speedup). Q-GPU also outperforms the state-of-the-art OpenMP CPU implementation, the Google Qsim-Cirq simulator, and the Microsoft QDK simulator by 1.49×, 2.02×, and 10.82×, respectively.
Portability in high-performance quantum-circuit simulation need not begin at the kernel. We present QSimAdv, which makes late binding, rather than a common kernel, the basis of vendor independence. Representation, operator lowering, and data placement are bound only when their required inputs become available. Before full-state allocation, circuit, noise, and output inspection can route eligible generic sampled-count requests to a stabiliser tableau; explicitly requested representations remain fixed. For full-state execution, backend constraints shape fusion; an ordered fused operator binds to a native lowering only after its physical targets are known. A first-class logical-to-physical layout map records non-canonical order across local and rank-address bits, so the dispatcher moves nonlocal targets only on demand. GPU, CPU, and Message Passing Interface (MPI) backends share these semantics while retaining native execution paths. We realize this design on NVIDIA GH200 and AMD MI250X/EPYC systems across local and distributed execution. With matched complex 32-bit floating-point state storage, QSimAdv leads both Aer Hopper configurations at N=32 and Aer's HIP backend at four shared MI250X sizes from N=24 to 30. Strong scaling exposes platform dependence: on setonix, QSimAdv leads both GPU and CPU comparisons at every measured rank, achieving 3.4× and 2.8× speedups, respectively, from one to eight ranks; neither the GH200 path nor the CPU path speeds up at eight ranks. Weak scaling reaches 256 ranks with 2 TiB GPU and 1 TiB CPU states. Together, these results support that portability can reside above the kernel boundary while execution remains native and extends across distributed memory.
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.