Benchmarking Noise Extrapolation with OpenPulse

Abstract

Distilling precise estimates from noisy intermediate scale quantum (NISQ) data has recently attracted considerable attention. In order to augment digital qubit metrics, such as gate fidelity, we discuss analog error mitigability, i.e. the ability to accurately distill precise observable estimates, as a hybrid quantum-classical computing benchmarking task. Specifically, we characterize single qubit error rates on IBM's Poughkeepsie superconducting quantum hardware, incorporate control-mediated noise dependence into a generalized rescaling protocol, and analyze how noise characteristics influence Richardson extrapolation-based error mitigation. Our results identify regions in the space of Hamiltonian control fields and circuit-depth which are most amenable to reliable noise extrapolation, as well as shedding light on how low-level hardware characterization can be used as a predictive tool for uncertainty quantification in error mitigated NISQ computations.

arXiv comment5 pages, 3 figures, comments welcome

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• Quantum Error Mitigation
• Hybrid quantum-classical algorithms and quantum error mitigation
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• Discriminating Quantum States with Quantum Machine Learning
• Simulating of X-states and the two-qubit XYZ Heisenberg system on IBM quantum computer
• Quantum Tunneling: From Theory to Error-Mitigated Quantum Simulation
• Circuit-Noise-Resilient Virtual Distillation
• DisQ: A Novel Quantum Output State Classification Method on IBM Quantum Computers using OpenPulse
• A Useful Metric for the NISQ Era: Qubit Error Probability and Its Role in Zero Noise Extrapolation