Data-efficient Quantum Noise Modeling Via Machine Learning Enables Accurate Noise Models from Existing Data

Predicting and mitigating errors represents a major challenge in harnessing the power of quantum computers, and accurate noise models are essential for building reliable systems. Yanjun Ji, Marco Roth from the Fraunhofer Institute for Manufacturing Engineering and Automation, David A. Kreplin from the same institution, and Ilia Polian from the University of Stuttgart, alongside Frank K. Wilhelm from Forschungszentrum Jülich and Saarland University, present a new framework that dramatically improves how these models are created. Their approach learns directly from existing quantum circuit data, bypassing the need for extensive and costly hardware characterisation. The team demonstrates that models trained on small circuits accurately predict behaviour in larger, more complex systems, achieving up to a 65% improvement in accuracy compared to conventional methods. This data-efficient technique offers a practical pathway towards building more robust and effective quantum computers by enabling better noise-aware compilation and error mitigation strategies.

Machine Learning Dramatically Reduces Noise Characterisation Needs

Accurate characterisation of noise is crucial for realising practical quantum computations, yet traditional methods often demand extensive experimental data. This work introduces a machine learning approach to significantly reduce the data requirements for quantum noise modelling. The method leverages Gaussian process regression to construct surrogate models of the noise, effectively interpolating between sparsely sampled data points and extrapolating to unmeasured regions of the parameter space. By incorporating prior knowledge about the noise structure, the team achieves high fidelity noise models with substantially fewer measurements compared to conventional techniques. This data efficiency enables more rapid and cost-effective characterisation of quantum devices, accelerating progress towards fault-tolerant quantum computing, and facilitates the development of improved noise mitigation strategies and more accurate simulations of quantum algorithms.

The research focuses on maximizing the computational utility of near-term quantum processors by creating predictive noise models that inform robust, noise-aware compilation and error mitigation. Conventional models often fail to capture the complex error dynamics of real hardware or require prohibitive characterization overhead. The team introduces a data-efficient, machine learning-based framework to construct accurate, parameterized noise models for superconducting quantum processors.

Sophisticated Noise Models Extend Quantum Algorithm Reach

This research explores how to improve the performance of near-term quantum algorithms by accurately modelling and mitigating noise. It investigates methods to address the inherent errors in current quantum hardware, known as NISQ devices, to extend the reach of quantum computation. The research specifically focuses on developing sophisticated noise models, employing error mitigation techniques without full quantum error correction, optimising algorithms, and rigorously benchmarking performance on various hardware platforms.

The research employs a multi-faceted approach, combining theoretical modelling, numerical simulations, and experimental validation on actual quantum hardware. The team explores advanced noise models that go beyond simple assumptions, incorporating time-varying and non-Markovian noise characteristics and considering cross-talk effects. Bayesian optimization is used for hyperparameter optimization of both noise models and quantum algorithms, allowing for efficient exploration of the parameter space. Machine learning techniques are employed to learn noise characteristics from experimental data, and quantum process tomography is used to characterize noise in qubits and validate the accuracy of the models.

The research demonstrates that more sophisticated noise models, incorporating time-varying and non-Markovian effects, can significantly improve the accuracy of predictions and the performance of quantum algorithms. The combination of accurate noise modelling and appropriate error mitigation techniques leads to substantial improvements in algorithm performance, extending the computational reach of NISQ devices. The use of Bayesian optimization to tune both noise models and algorithm hyperparameters results in optimized performance on real hardware, validated through experiments on IBM Quantum devices.

This research has significant implications for the field of quantum computing, providing valuable tools and techniques for improving the performance of near-term quantum devices and bringing us closer to practical quantum applications. It addresses the critical challenge of accurately modelling and mitigating noise in real quantum hardware, enabling the implementation of more complex quantum algorithms and guiding the development of more robust and reliable quantum hardware.

Several avenues for future research include exploring even more complex noise models, developing adaptive error mitigation techniques, integrating noise modelling with advanced quantum control techniques, investigating the scalability of the proposed techniques to larger quantum systems, and applying the techniques to a wider range of quantum algorithms and applications. Further work should also focus on hardware-aware algorithm design and developing automated methods for characterizing noise in quantum devices.

In summary, this document presents a comprehensive and rigorous investigation of noise modelling and error mitigation for near-term quantum computing. The research provides valuable insights and tools for improving the performance of quantum algorithms on real hardware, paving the way for more practical quantum applications.

Improved Noise Prediction With Machine Learning

This research presents a novel, parameterized noise model for superconducting quantum processors that significantly improves the accuracy of predicting circuit behaviour. By repurposing data from standard circuit executions, the team developed a machine learning approach to construct high-fidelity models without the need for extensive, time-consuming characterization protocols. Results demonstrate up to a 65% reduction in the Hellinger distance, a measure of divergence between predicted and experimental quantum state distributions, confirming that these models more accurately represent algorithm performance on real hardware. This improvement in model fidelity is crucial for developing effective noise-aware compilation strategies, enabling optimisation of qubit selection and gate insertion for enhanced algorithm performance. The framework’s adaptability has been validated across multiple IBM quantum devices and algorithms, highlighting its broad potential for adoption.