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Quantum circuit simulators have a long tradition of exploiting massivehardware parallelism. Most of the times, parallelism has been supported byspecial purpose libraries tailored specifically for the quantum circuits.Quantum circuit simulators are integral part of quantum software stacks, whichare mostly written in Python. Our focus has been on ease of use, implementationand maintainability within the Python ecosystem. We report the performancegains we obtained by using CuPy, a general purpose library (linear algebra)developed specifically for CUDA-based GPUs, to simulate quantum circuits. Forsupremacy circuits the speedup is around 2x, and for quantum multipliers almost22x compared to state-of-the-art C++-based simulators.

Fast quantum circuit simulation using hardware accelerated general purpose libraries