Enormous-Scale Quantum State Vector Calculation with FPGA-accelerated SATA storages

Journal article

Ryohei Niwase, Hikaru Harasawa, Yoshiki Yamaguchi, Kaijie Wei, Hideharu Amano, Takefumi Miyoshi
International Conference on Field-Programmable Technology, 2023

Semantic Scholar DBLP DOI

Cite

APA Click to copy
Niwase, R., Harasawa, H., Yamaguchi, Y., Wei, K., Amano, H., & Miyoshi, T. (2023). Enormous-Scale Quantum State Vector Calculation with FPGA-accelerated SATA storages. International Conference on Field-Programmable Technology.

Chicago/Turabian Click to copy
Niwase, Ryohei, Hikaru Harasawa, Yoshiki Yamaguchi, Kaijie Wei, Hideharu Amano, and Takefumi Miyoshi. “Enormous-Scale Quantum State Vector Calculation with FPGA-Accelerated SATA Storages.” International Conference on Field-Programmable Technology (2023).

MLA Click to copy
Niwase, Ryohei, et al. “Enormous-Scale Quantum State Vector Calculation with FPGA-Accelerated SATA Storages.” International Conference on Field-Programmable Technology, 2023.

BibTeX Click to copy

@article{ryohei2023a,
  title = {Enormous-Scale Quantum State Vector Calculation with FPGA-accelerated SATA storages},
  year = {2023},
  journal = {International Conference on Field-Programmable Technology},
  author = {Niwase, Ryohei and Harasawa, Hikaru and Yamaguchi, Yoshiki and Wei, Kaijie and Amano, Hideharu and Miyoshi, Takefumi}
}

Abstract

Gate-based quantum computers, poised to surpass their classical counterparts, drive intense research interest. Emulating quantum systems relies on state vector-type simulators, hampered by soaring memory demands, especially for systems beyond 40 qubits. Our solution called Trefoil system combines multiple FPGAs and SATA disks. Trefoil features a main unit and eight storage subsystems, each with 32 SATA disks, providing 2 PBytes of memory for up to 47 qubits. Sector-level block transfers enable efficient quantum operations such as H, Z, CNOT, and unitary matrices. The results show an H gate for 30 qubits executes in 10 minutes without increased times using parallel processing. Our study advances scalable quantum computing simulations, surmounting memory limitations.