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  • Open Access

Sparse non-Markovian Noise Modeling of Transmon-Based Multi-Qubit Operations

Yasuo Oda1, Kevin Schultz2, Leigh Norris2, Omar Shehab3, and Gregory Quiroz1,2,*

  • *Contact author: gregory.quiroz@jhuapl.edu
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PRX Quantum 7, 020327 – Published 12 May, 2026

DOI: https://doi.org/10.1103/lx8x-z29x

Abstract

The influence of noise on quantum dynamics is one of the main factors preventing current quantum processors from performing accurate quantum computations. Sufficient noise characterization and modeling can provide key insights into the effect of noise on quantum algorithms and inform the design of targeted error protection protocols. However, constructing effective noise models that are sparse in model parameters, yet predictive can be challenging. In this work, we present an approach for effective noise modeling of multi-qubit operations on transmon-based devices. Through a comprehensive characterization of seven devices offered by the IBM Quantum Platform, we show that the model can capture and predict a wide range of single- and two-qubit behaviors, including non-Markovian effects resulting from spatiotemporally correlated noise sources. The model’s predictive power is further highlighted through multi-qubit dynamical decoupling demonstrations and an implementation of the variational quantum eigensolver. As a training proxy for the hardware, we show that the model can predict expectation values within a relative error of 0.5%; this is a sevenfold improvement over default hardware noise models. Through these demonstrations, we highlight key error sources in superconducting qubits and illustrate the utility of reduced noise models for predicting hardware dynamics.

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Popular Summary

As quantum computers scale, accurately characterizing and managing noise remains a central challenge. Noise arises from unavoidable interactions between a quantum system and its environment, degrading algorithm performance and limiting scalability. Effective noise models can be critical for designing targeted error protection strategies and advancing toward quantum advantage.

In this work, we introduce a comprehensive noise modeling approach for transmon-based quantum processors, focusing on modern publicly available devices. Our method captures complex, non-Markovian noise with spatial and temporal correlations while maintaining a balance between simplicity and predictive accuracy. We validate the model across 39 qubits on seven IBM Quantum cloud-accessible devices, demonstrating strong agreement with a range of nontrivial quantum dynamics, including single- and two-qubit gate characterization protocols and multi-qubit dynamical decoupling sequences. When used as a hardware proxy for variational quantum algorithms, the model improves predictive accuracy by a factor of 7 over existing approaches.

Our results provide insight into dominant noise sources in current quantum processors, highlighting the role of correlated effects such as qubit crosstalk and time-dependent environmental fluctuations. More broadly, this work establishes a unified and experimentally validated framework that links device-level noise to circuit- and algorithm-level behavior. By combining accuracy with a reduced parameterization, it enables predictive modeling of complex quantum dynamics without requiring low-level hardware access, offering a practical path toward scalable characterization and control of quantum systems.

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PRX Quantum 7, 020327– Published 12 May, 2026
  • Received 20 December 2024
  • Revised 2 January 2026
  • Accepted 17 March 2026
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