New Probabilistic Graybox Model Boosts Uncertainty Characterization in Quantum Devices
Researchers Akshay K. Vijayakumar, Michael K. Waters, and Ben Criger have developed a probabilistic Graybox model to enhance uncertainty characterization in quantum devices. This breakthrough addresses a significant challenge in quantum device performance assessment, requiring methods that accurately capture both expected behaviour and inherent uncertainties.
The team's model, built using Bayesian Neural Networks, demonstrates superior performance. It accurately captures the distribution of observed data, outperforming the original Graybox method by up to 1.9 times. This advancement overcomes a key limitation of traditional Graybox methods by incorporating uncertainty quantification, enabling researchers to make more informed decisions based on experimental data.
In parallel, Poramet Pathumsoot, Michal Hajdušek, and Rodney Van Meter from Keio University have developed a new probabilistic approach to 'Graybox' characterisation. Their method combines known system dynamics with unknown transformations, leveraging the strengths of both 'Whitebox' and 'Blackbox' modeling approaches. This intersection of quantum computing and machine learning is rapidly advancing, driven by the need to overcome challenges in building reliable quantum technologies.
The probabilistic Graybox model promises to improve the reliability and interpretability of quantum experiments. It outperforms previous models by up to 1.9 times in capturing observed data distributions, significantly enhancing predictive accuracy and uncertainty quantification. This advancement paves the way for more robust quantum technologies.
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