Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session G35: Quantum Characterization, Verification, and Validation: Noise and Cross-Talk
11:30 AM–2:30 PM,
Tuesday, March 15, 2022
Room: McCormick Place W-193B
Sponsoring
Unit:
DQI
Chair: Senrui Chen, University of Chicago
Abstract: G35.00004 : Tomographic construction and prediction of superconducting qubit dynamics using the post-Markovian master equation*
12:06 PM–12:18 PM
Presenter:
Haimeng Zhang
(University of Southern California)
Authors:
Haimeng Zhang
(University of Southern California)
Bibek B Pokharel
(Univ of Southern California)
Eli Levenson-Falk
(Univ of Southern California)
Daniel A Lidar
(University of Southern California)
Collaboration:
The authors would like to acknowledge IBM Quantum services for this work.
Non-Markovian noise presents a particularly relevant challenge in understanding and combating decoherence in quantum computers. Using tomographic constructed state dynamics of a superconducting qubit system, we show that we can construct a phenomenological dynamical model using the post-Markovian master equation (PMME). We experimentally test our protocol to characterize the free evolution of a single qubit in one of IBMQ's cloud-based quantum processors. The resultant PMME model characterizes the cross-talk effect due to the neighboring qubits, the timescales of the qubit decoherence and dissipation process, and quantifies the degree of non-Markovianity of the system. We also demonstrate that the constructed PMME model can predict future qubit dynamics for an arbitrary single-qubit state better than the standard Lindblad model. Our model construction protocol requires sampling the qubit evolution at multiple time points for only one qubit initial state; thus, it requires less data than the process tomography and machine learning methods. The PMME has a closed-form analytical solution, making it straightforward to find the best-fit PMME model parameters via the maximum likelihood estimation method. Our protocol provides a robust estimation method for a continuous dynamical model beyond the commonly assumed Markovian approximation, leading to more accurate modeling of noisy intermediate-scale quantum (NISQ) devices.
*This work is funded by the NSF under grant OMA-1936388.
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