Abstract—This work presents a portable, modular quantum-circuit emulator designed to accelerate algorithm prototyping by leveraging Central Processing Unit (CPU) and Compute Unified Device Architecture (CUDA)-enabled Graphic Processing Unit (GPU) backends for dense linear-algebra workloads. The emulator provides a lightweight backend abstraction that unifies NumPy and CuPy, supports both statevector and density-matrix representations, and implements efficient multi-target unitary embedding and Kraus-map noise channels. These capabilities enable rapid exploration of quantum algorithms that require full 2ⁿ × 2ⁿ operators, such as quantum phase estimation, full Quantum Fourier Transform (QFT), and noise-aware variational circuits, while minimizing host/device transfer and preserving numerical parity between backends. Implementation strategies are described for embedding single- and multi-qubit gates, parameterized rotations, and controlled unitaries, along with an “apply_unitary_on_targets” routine that employs reshape/matrix-multiply/reshape patterns to map arbitrary target sets to dense GPU kernels for high throughput. Using a curated benchmark suite of dense quantum workloads and microbenchmarks, including large matrix multiplies and Fast Fourier Transforms (FFT), performance is quantified across a range of qubit counts and precisions. The results demonstrate substantial speedups on modern NVIDIA GPUs for workloads dominated by dense linear algebra, identify cross-over points where GPU acceleration becomes beneficial, and analyse memory/precision trade-offs for complex64 versus complex128. The presented emulator lowers the barrier for noise-aware algorithm prototyping by combining practical software ergonomics with GPU performance, enabling faster iteration on algorithm design and noise-mitigation strategies on commodity single-node hardware. While the abstraction layer ensures portability, achieving peak performance currently requires hardware-specific tuning—a limitation we address through future automation strategies.
 
Keywords—quantum circuit simulation, Graphic Processing Unit (GPU) acceleration, statevector simulation, density-matrix simulation, quantum noise modelling, Kraus map


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