TY - GEN
T1 - Adaptive mitigation of time-varying quantum noise
AU - Dasgupta, Samudra
AU - Humble, Travis S.
AU - Danageozian, Arshag
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Current quantum computers suffer from non-stationary noise channels with high error rates, which undermines their reliability and reproducibility. We propose a Bayesian inference based adaptive algorithm that can learn and mitigate quantum noise in response to changing channel conditions. Our study emphasizes the need for dynamic inference of critical channel parameters to improve program accuracy. We use the Dirichlet distribution to model the stochasticity of the Pauli channel. This allows us to perform Bayesian inference, which can improve the performance of probabilistic error cancellation (PEC) under time-varying noise. Our work demonstrates the importance of characterizing and mitigating temporal variations in quantum noise, which is crucial for developing more accurate and reliable quantum technologies. Our results demonstrate that Bayesian PEC can outperform non-adaptive approaches by a factor of 4.5x when measured using Hellinger distance from the ideal distribution.
AB - Current quantum computers suffer from non-stationary noise channels with high error rates, which undermines their reliability and reproducibility. We propose a Bayesian inference based adaptive algorithm that can learn and mitigate quantum noise in response to changing channel conditions. Our study emphasizes the need for dynamic inference of critical channel parameters to improve program accuracy. We use the Dirichlet distribution to model the stochasticity of the Pauli channel. This allows us to perform Bayesian inference, which can improve the performance of probabilistic error cancellation (PEC) under time-varying noise. Our work demonstrates the importance of characterizing and mitigating temporal variations in quantum noise, which is crucial for developing more accurate and reliable quantum technologies. Our results demonstrate that Bayesian PEC can outperform non-adaptive approaches by a factor of 4.5x when measured using Hellinger distance from the ideal distribution.
KW - Adaptive mitigation
KW - Computational accuracy
KW - Device reliability
KW - NISQ hardware-software co-design
KW - Probabilistic error cancellation
KW - Result reproducibility
KW - Spatio-temporal non-stationarity
KW - Time-varying quantum noise
UR - http://www.scopus.com/inward/record.url?scp=85180014591&partnerID=8YFLogxK
U2 - 10.1109/QCE57702.2023.00020
DO - 10.1109/QCE57702.2023.00020
M3 - Conference contribution
AN - SCOPUS:85180014591
T3 - Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023
SP - 99
EP - 110
BT - Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023
A2 - Muller, Hausi
A2 - Alexev, Yuri
A2 - Delgado, Andrea
A2 - Byrd, Greg
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Quantum Computing and Engineering, QCE 2023
Y2 - 17 September 2023 through 22 September 2023
ER -