Researchers from the Johns Hopkins Applied Physics Laboratory (APL) and Johns Hopkins University have developed a new framework for modeling noise in superconducting quantum processors.
The study, published in the journal PRX Quantum, demonstrates a sevenfold improvement in predictive accuracy over existing methods. The new approach provides a practical way to understand and predict errors that affect quantum computers.
Quantum computers use quantum bits(qubits), to process information. Unlike traditional computer bits that are either 0 or 1, qubits can exist in multiple states at the same time. This property gives quantum computers the potential to solve certain problems much faster than conventional machines.
However, qubits are extremely sensitive to their surroundings. Small disturbances from electrical signals, magnetic fields, or temperature changes can introduce noise into a quantum system. This noise creates errors that reduce the accuracy of quantum calculations.
Understanding and managing these errors remains one of the biggest challenges in quantum computing. Scientists need accurate noise models to design better hardware, develop more robust error-correction methods, and create algorithms that operate reliably. Without effective noise management, building large-scale fault-tolerant quantum computers remains difficult.
The Johns Hopkins team focused on superconducting quantum processors based on transmon qubits. Transmons are among the most widely used qubits in the quantum computing industry because they are less sensitive to electrical charge fluctuations than earlier superconducting designs. Major quantum computing platforms rely on this technology for many of their systems.
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To conduct the research, the team used cloud-based access to seven superconducting quantum devices, each containing 39 qubits.
This approach allowed them to study real-world quantum processors without direct access to the underlying hardware. The setup closely matched the experience of most quantum computing users, who typically interact with quantum systems through cloud services.
Noise Models Drive Quantum Accuracy
Working without low-level hardware access presented several challenges. The researchers could not directly observe many of the processors’ internal characteristics. Instead, they developed a method that focused on measuring how errors accumulated during repeated quantum operations.
The team repeatedly ran computations on the quantum processors and analyzed the results. By observing how often the outputs deviated from expected values, they identified patterns in the underlying noise. These patterns provided important clues about the physical processes affecting qubit performance.
A key feature of the new framework is its ability to describe two major categories of quantum errors within a single model. The first category is incoherent errors, which occur when quantum information is permanently lost. The second category is coherent errors, which often result from calibration problems or control imperfections and can sometimes be corrected.
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Previous studies have typically examined these two error types separately. This separation can limit the accuracy of predictions because real quantum systems often experience both types simultaneously. The Johns Hopkins framework combines them into one unified model that captures a broader range of behavior.
Researchers say the model remains relatively simple despite covering multiple noise mechanisms. It requires only a few parameters while still providing detailed predictions of system performance. This balance between simplicity and accuracy is one of the model’s most important strengths.
The framework also demonstrated the ability to predict the performance of small quantum algorithms. This capability is important because quantum computing developers need tools that connect the behavior of physical hardware to application-level results. Better predictions can help engineers understand how hardware limitations affect practical computations.
Project leader Gregory Quiroz said the goal was to create a model that predicts many different behaviors without requiring a highly complex description of every quantum interaction.
He explained that the framework connects multiple noise sources into a single experimentally validated system. This allows researchers to generate more reliable predictions while keeping the model manageable.
Lead author Yasuo Oda said the team had to develop creative strategies to work with cloud-accessible quantum processors. Instead of focusing on individual operations, they studied how errors accumulated across many computations. This approach enabled them to extract meaningful information about the devices despite limited hardware visibility.
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The new model is expected to support improvements across the entire quantum computing stack. The quantum computing stack includes hardware components, software tools, algorithms, and error-correction systems. Insights from the model can help researchers optimize each of these layers.
The work is part of the SMART Stack project led by APL. The initiative focuses on developing scalable, adaptable methods for characterizing and managing quantum errors across diverse quantum computing platforms. The project aims to make quantum systems easier to understand, maintain, and improve.
Several organizations are collaborating on the effort. Partners include the University of Chicago, the University of Michigan, the Unitary Foundation, Lawrence Livermore National Laboratory, and Infleqtion. The project receives funding through a competitive quantum computing award from the US Department of Energy.
Quantum computing is still in its early stages of development, but advances in error modeling are important. As processors grow larger and more powerful, understanding noise at a deeper level will be essential for achieving reliable performance. Accurate models help researchers identify weaknesses before they become major barriers.
The significance of this work extends beyond a single quantum hardware platform. Many quantum computing technologies face similar challenges related to noise and error accumulation. Techniques that improve error characterization can benefit a wide range of future quantum systems.
Better noise models support stronger error correction, more efficient algorithms, and smarter hardware design. The Johns Hopkins framework provides a new foundation for these efforts and brings the field closer to dependable large-scale quantum computing.
