TY - GEN

T1 - How many trials do we need for reliable NISQ computing?

AU - Tanimoto, Teruo

AU - Matsuo, Shuhei

AU - Kawakami, Satoshi

AU - Tabuchi, Yutaka

AU - Hirokawa, Masao

AU - Inoue, Koji

N1 - Funding Information:
This work was partly supported by JSPS KAKENHI Grant Number JP19H01105, JST-Mirai Program Grant Number JP18077278, and MEXT Quantum Leap Flagship Program (MEXT Q-LEAP) Grant Number JPMXS0118068682. The computation resource of this work is partly provided by RIIT, Kyushu University.
Funding Information:
This work was partly supported by JSPS KAK-ENHI Grant Number JP19H01105, JST-Mirai Program Grant Number JP18077278, and MEXT Quantum Leap Flagship Program (MEXT Q-LEAP) Grant Number JP-MXS0118068682. The computation resource of this work is partly provided by RIIT, Kyushu University.

PY - 2020/7

Y1 - 2020/7

N2 - Gate-based quantum computing is an attractive candidate in the post-Moore era. Noisy intermediate-scale quantum (NISQ) computers are expected to be available in the next few years. It is required to repeatedly execute the target quantum application for reliable NISQ computing, e.g., users can set 1,024 as a repetition parameter in the IBM-Q machine, because NISQ computers output follows the probability distribution of execution trials. Since the distribution depends strongly on the effects of noise, it is difficult to determine a sufficient number of repetitions. This paper proposes a novel statistical approach for efficient NISQ computing. The key idea is to introduce a Bayesian credible interval model to obtain convergence of the probability distributions. We demonstrate that our execution method can detect all significant output values, that occur more often than the random situation (probability is 1/2n), using a NISQ simulator.

AB - Gate-based quantum computing is an attractive candidate in the post-Moore era. Noisy intermediate-scale quantum (NISQ) computers are expected to be available in the next few years. It is required to repeatedly execute the target quantum application for reliable NISQ computing, e.g., users can set 1,024 as a repetition parameter in the IBM-Q machine, because NISQ computers output follows the probability distribution of execution trials. Since the distribution depends strongly on the effects of noise, it is difficult to determine a sufficient number of repetitions. This paper proposes a novel statistical approach for efficient NISQ computing. The key idea is to introduce a Bayesian credible interval model to obtain convergence of the probability distributions. We demonstrate that our execution method can detect all significant output values, that occur more often than the random situation (probability is 1/2n), using a NISQ simulator.

UR - http://www.scopus.com/inward/record.url?scp=85090420736&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85090420736&partnerID=8YFLogxK

U2 - 10.1109/ISVLSI49217.2020.00059

DO - 10.1109/ISVLSI49217.2020.00059

M3 - Conference contribution

AN - SCOPUS:85090420736

T3 - Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI

SP - 288

EP - 290

BT - Proceedings - 2020 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2020

PB - IEEE Computer Society

T2 - 19th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2020

Y2 - 6 July 2020 through 8 July 2020

ER -