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The learnability of Pauli noise
Senrui Chen1, Yunchao Liu2, Matthew Otten3
1Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL, 60637, USA. csenrui@uchicago.edu.
Nature Communications
|January 4, 2023
Summary
This study precisely characterizes learnable Pauli noise on quantum gates using graph theory. Cycle benchmarking optimally extracts all learnable noise information, providing bounds for unlearnable aspects.
Area of Science:
- Quantum Information Science
- Quantum Computing
- Quantum Error Characterization
Background:
- Current quantum benchmarking algorithms aim to characterize noise in quantum gates.
- A key challenge is understanding the limits of learnable noise due to gauge freedom, even for simple gates like CNOT.
Purpose of the Study:
- To precisely characterize the learnability of Pauli noise channels on Clifford gates.
- To determine the optimal benchmarking strategy for extracting learnable noise information.
Main Methods:
- Utilized graph theoretical tools to analyze Pauli noise channels on Clifford gates.
- Experimentally demonstrated noise characterization on an IBM CNOT gate.
- Investigated the impact of state preparation noise on learnable and unlearnable degrees of freedom.
Main Results:
- Provided a precise characterization of Pauli noise learnability for Clifford gates.
- Demonstrated that cycle benchmarking is optimal for extracting all learnable Pauli noise information.
- Identified up to 2 unlearnable degrees of freedom for an IBM CNOT gate and established bounds using physical constraints.
Conclusions:
- Cycle benchmarking is the optimal method for extracting all learnable Pauli noise information from quantum gates.
- Unlearnable noise components can be bounded using physical constraints, and attempts to ignore state preparation noise lead to unphysical estimates.
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