While existing quantum hardware resources have limited availability and reliability, there is a growing demand for exploring and verifying quantum algorithms. Efficient classical simulators for high-performance quantum simulation are critical to meeting this demand. However, due to the vastly varied characteristics of classical hardware, implementing hardware-specific optimizations for different hardware platforms is challenging. To address such needs, we propose CAST (Cross-platform Adaptive Schrödinger-style Simulation Toolchain), a novel compilation toolchain with cross-platform (CPU and Nvidia GPU) optimization and high-performance backend supports. CAST exploits a novel sparsity-aware gate fusion algorithm that automatically selects the best fusion strategy and backend configuration for targeted hardware platforms. CAST also aims to offer versatile and high-performance backend for different hardware platforms. To this end, CAST provides an LLVM IR-based vectorization optimization for various CPU architectures and instruction sets, and a PTX-based code generator for Nvidia GPU support. We benchmark CAST against IBM Qiskit, Google QSimCirq, Nvidia cuQuantum backend, and other high-performance simulators. On various 32-qubit CPU-based benchmarks, CAST achieves up to 8.03x speedup than Qiskit. On various 30-qubit GPU-based benchmarks, CAST achieves up to 39.3x speedup than Nvidia cuQuantum backend.
While existing quantum hardware resources have limited availability and reliability, there is a growing demand for exploring and verifying quantum algorithms. Efficient classical simulators for high-performance quantum simulation are critical to meeting this demand. However, due to the vastly varied characteristics of classical hardware, implementing hardware-specific optimizations for different hardware platforms is challenging. To address such needs, we propose CAST (Cross-platform Adaptive Schrödinger-style Simulation Toolchain), a novel compilation toolchain with cross-platform (CPU and Nvidia GPU) optimization and high-performance backend supports. CAST exploits a novel sparsity-aware gate fusion algorithm that automatically selects the best fusion strategy and backend configuration for targeted hardware platforms. CAST also aims to offer versatile and high-performance backend for different hardware platforms. To this end, CAST provides an LLVM IR-based vectorization optimization for various CPU architectures and instruction sets, and a PTX-based code generator for Nvidia GPU support. We benchmark CAST against IBM Qiskit, Google QSimCirq, Nvidia cuQuantum backend, and other high-performance simulators. On various 32-qubit CPU-based benchmarks, CAST achieves up to 8.03x speedup than Qiskit. On various 30-qubit GPU-based benchmarks, CAST achieves up to 39.3x speedup than Nvidia cuQuantum backend.
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.
Recent developments in quantum hardware, including the scaling of physical qubits and advanced quantum error correction techniques, have increased the number of reliable logical qubits. However, this progress has introduced new challenges for quantum algorithm developers. Limited access to physical quantum machines and the insufficient performance of classical quantum simulators for near-term scales (∼30 logical qubits) hinder the simulation and validation of quantum algorithms. To address this urgent need for improving simulation performance, we propose a novel end-to-end full-stack solution for Schr¨odinger-style simulation that jointly explores algorithm, software, and hardware optimizations. At the algorithmic level, by identifying the inefficiency in executing complex signed permutations and complex unitary permutation gates, we introduce index redirection and pre-compute merging that significantly reduce data movement and computational complexity. At the hardware level, we propose a reconfigurable dataflow architecture with adaptive memory scheduling and swapping optimizations. At the software level, an end-to-end toolchain is introduced to jointly explore both algorithmic and hardware optimizations. A comprehensive evaluation across a large suite of quantum circuits demonstrates that our work achieves a maximum speedup exceeding 50× over the GPU-based Qiskit baseline.
Recent developments in quantum hardware, including the scaling of physical qubits and advanced quantum error correction techniques, have increased the number of reliable logical qubits. However, this progress has introduced new challenges for quantum algorithm developers. Limited access to physical quantum machines and the insufficient performance of classical quantum simulators for near-term scales (∼30logical qubits) hinder the simulation and validation of quantum algorithms. To address this urgent need for improving simulation performance, we propose a novel end-to-end full-stack solution for Schrödingerstyle simulation that jointly explores algorithm, software, and hardware optimizations. At the algorithmic level, by identifying the inefficiency in executing complex signed permutations and complex unitary permutation gates, we introduce index redirection and pre-compute merging that significantly reduce data movement and computational complexity. At the hardware level, we propose a reconfigurable dataflow architecture with adaptive memory scheduling and swapping optimizations. At the software level, an end-to-end toolchain is introduced to jointly explore both algorithmic and hardware optimizations. A comprehensive evaluation across a large suite of quantum circuits demonstrates that our work achieves a maximum speedup exceeding50×over the GPU-based Qiskit baseline.
Quantum circuit simulation is essential for testing and developing quantum algorithms, and recent advances have produced a range of simulators with diverse characteristics. Quantum gate fusion is a powerful optimization technique that improves simulation performance by reducing memory access and computational load. However, existing gate fusion methods usually overlook the performance characteristics of underlying simulation environments, resulting in suboptimal results. In this work, we propose a novel gate fusion compiler framework, qfusion-opt, to address this challenge. By using microbenchmarks to characterize performance across different simulation environments, qfusion-opt adaptively tailors circuit optimizations to the target platform. This approach enables higher simulation performance without requiring detailed knowledge of the underlying simulator or hardware. Experimental results show that qfusion-opt consistently improves performance across different simulation environments on quantum benchmark circuits, achieving up to 9.6x speedup on Qiskit-Aer and 3.0x speedup on Queen compared to the original circuits without gate fusion. Overall, qfusion-opt provides a practical step toward high performance quantum circuit simulation across different platforms.
Quantum circuit simulation is crucial for the development of quantum algorithms, particularly given the high cost and noise limitations of physical quantum hardware. While full-state quantum circuit simulation is commonly employed for prototyping and debugging, it poses challenges because of the exponential increase in simulation time for large quantum systems. In this work, we propose an extensible framework designed to enhance simulation performance by optimizing both data locality and computational efficiency, thereby addressing these challenges. This framework is seamlessly integrated with an optimizer that restructures quantum circuits and a simulator that adjusts execution strategies for various quantum operations. For the newly developed components, merge booster and diagonal detector, the underlying algorithms are inspired by the principles of quantum entanglement and gate fusion, as well as by the limitations identified in existing third-party simulation libraries. The experiments were conducted on eight DGX-H100 workstations, each equipped with eight NVIDIA H100 GPUs, employing both gate-level and circuit-level benchmarks. The results indicate a speedup of up to 160 times for circuit-level benchmarks and an acceleration of up to 34 times for diagonal-heavy gate-level benchmarks compared to existing simulators. The proposed methodologies are anticipated to deliver more robust and faster quantum circuit simulations, thereby fostering the advancement of novel quantum algorithms.
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.