PAEMS: Precise and Adaptive Error Model for Superconducting Quantum Processors

TL;DR

This paper introduces PAEMS, an adaptive error model for superconducting quantum processors that significantly improves error characterization and surpasses previous models in accuracy across multiple platforms.

Contribution

PAEMS provides a novel, precise, and adaptive qubit error modeling framework that captures error evolutions more accurately than classic models, enhancing quantum error correction.

Findings

PAEMS reduces error correlation metrics by up to 19.5 times.

PAEMS outperforms Google's SI1000 error model by 58-73%.

Experiments on multiple quantum platforms validate PAEMS's superior accuracy.

Abstract

Superconducting quantum processor units (QPUs) are incapable of producing massive datasets for quantum error correction (QEC) because of hardware limitations. Thus, QEC decoders heavily depend on synthetic data from qubit error models. Classic depolarizing error models with polynomial complexity present limited accuracy. Coherent density matrix methods suffer from exponential complexity $\propto O(4^n)$ where $n$ represents the number of qubits. This paper introduces PAEMS: a precise and adaptive qubit error model. Its qubit-wise separation framework, incorporating leakage propagation, captures error evolvements crossing spatial and temporal domains. Utilizing repetition-code experiment datasets, PAEMS effectively identifies the intrinsic qubit errors through an end-to-end optimization pipeline. Experiments on IBM's QPUs have demonstrated a 19.5$\times$, 9.3$\times$, and 5.2$\times$ reduction in timelike, spacelike, and spacetime error correlation, respectively, surpassing all of the previous works. It also outperforms the accuracy of Google's SI1000 error model by 58$\sim$73\% on multiple quantum platforms, including IBM's Brisbane, Sherbrooke, and Torino, as well as China Mobile's Wuyue and China Telecom's Tianyan.