Researchers at the Johns Hopkins Applied Physics Laboratory (APL) and Johns Hopkins University have achieved a sevenfold improvement in predicting noise within superconducting quantum processors, a critical step toward building stable and reliable quantum computers. Their new noise-modeling framework, detailed in the journal PRX Quantum, focuses on accurately characterizing interference that arises from environmental factors impacting qubits. “To really advance the field, we need models that can predict a wide range of behavior while utilizing a small number of parameters,” said project lead Gregory Quiroz, a senior physicist at APL and an associate research professor at the Johns Hopkins University Krieger School of Arts and Sciences. The team’s work, utilizing cloud access to 39 qubits across seven devices, offers a practical methodology for understanding noise in increasingly common, proprietary quantum systems.
Transmon Qubit Noise Analysis on Cloud Platforms
Their newly developed framework specifically targets superconducting quantum processors, a prevalent architecture utilizing transmon qubits, prized for their relative resilience to electrical charge noise, and addresses a critical bottleneck in building practical quantum computers. Published in PRX Quantum, the research details a method for characterizing noise in multi-qubit systems accessed via cloud platforms. Researchers confronted the challenge of studying noise without direct, low-level hardware access, a scenario increasingly common for users of proprietary quantum systems. To circumvent this limitation, the team ran repeated computations, accumulating errors to reveal underlying noise characteristics. This approach allowed them to characterize both incoherent errors, where information is lost, and coherent errors, which can be corrected with hardware calibration.
Significantly, the framework integrates these typically isolated error types into a single, predictive model. Yasuo Oda, the paper’s first author and a postdoctoral researcher, stated that they were able to combine a wide variety of errors into one model, which is simple in terms of parameters but also comprehensive in the types of phenomena it can describe, even predicting the performance of small quantum algorithms. The team’s work, part of the larger SMART Stack project, aims to create scalable and adaptable error management tools for quantum computing, ultimately informing improvements across the entire quantum computing stack, from hardware design to algorithm development.
Now that we have this low-weight noise model, we have the opportunity to apply it across all levels of the quantum computing stack, from hardware design to algorithm design to error correction.
Researchers are refining methods for characterizing the inherent instability of qubits, the fundamental building blocks of quantum computers, with a newly developed noise-modeling framework published in PRX Quantum. This approach achieves a sevenfold improvement in predictive accuracy compared to existing models, a substantial leap forward in the quest for reliable quantum computation. The innovation lies in a unified framework that connects multiple noise mechanisms, yielding a coherent predictive methodology.
APL is committed to characterizing and mitigating quantum noise and errors at every level of the quantum computing stack, including hardware, software, and hybrid computing systems combining quantum and classical computers.
“Actual quantum computer users won’t have low-level hardware access either—they’ll just be running applications, and they’ll need to be confident that they’re running correctly,” Quiroz noted, highlighting the practical relevance of their approach. The framework integrates both incoherent and coherent errors into a single model, a departure from traditional approaches that treat them in isolation.
The information we can get from the model can inform every level of the quantum computing stack.
SMART Stack Project Advances Quantum Error Management
The pursuit of reliable quantum computation received a boost with a newly developed noise-modeling framework, promising a significant leap in predictive accuracy for superconducting quantum processors. This framework doesn’t merely identify noise; it anticipates its effects, enabling more robust algorithms and error-correction strategies. Recognizing that practical users will lack low-level hardware control, the researchers deliberately mirrored real-world conditions. To overcome the limitations of remote access, they employed a technique of accumulating errors through repeated computations, allowing them to infer the underlying physical processes. This new model integrates both incoherent and coherent errors, typically studied separately, into a unified predictive methodology.
Fundamentally, we’re trying to drive a transition in a system of qubits from one state to another – in other words, to perform a quantum computation – and study how noise affects the success of that operation.
