Fast quantum circuit simulation using hardware accelerated general purpose libraries

Abstract

Quantum circuit simulators have a long tradition of exploiting massive hardware parallelism. Most of the times, parallelism has been supported by special purpose libraries tailored specifically for the quantum circuits. Quantum circuit simulators are integral part of quantum software stacks, which are mostly written in Python. Our focus has been on ease of use, implementation and maintainability within the Python ecosystem. We report the performance gains we obtained by using CuPy, a general purpose library (linear algebra) developed specifically for CUDA-based GPUs, to simulate quantum circuits. For supremacy circuits the speedup is around 2x, and for quantum multipliers almost 22x compared to state-of-the-art C++-based simulators.

arXiv commentaccepted at 2nd International Workshop on Quantum Computing: Circuits Systems Automation and Applications (QC-CSAA) in conjuction with the ISVLSI 2021

References in corpus (6)

• Characterizing Quantum Supremacy in Near-Term Devices
• Qulacs: a fast and versatile quantum circuit simulator for research purpose
• PennyLane: Automatic differentiation of hybrid quantum-classical computations
• Leveraging Secondary Storage to Simulate Deep 54-qubit Sycamore Circuits
• The challenge and opportunities of quantum literacy for future education and transdisciplinary problem-solving
• GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm