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- W4363671703 abstract "Quantum machine learning is a promising programming paradigm for the optimization of quantum algorithms in the current era of noisy intermediate scale quantum (NISQ) computers. A fundamental challenge in quantum machine learning is generalization, as the designer targets performance under testing conditions, while having access only to limited training data. Existing generalization analyses, while identifying important general trends and scaling laws, cannot be used to assign reliable and informative error bars to the decisions made by quantum models. In this article, we propose a general methodology that can reliably quantify the uncertainty of quantum models, irrespective of the amount of training data, of the number of shots, of the ansatz, of the training algorithm, and of the presence of quantum hardware noise. The approach, which builds on probabilistic conformal prediction, turns an arbitrary, possibly small, number of shots from a pre-trained quantum model into a set prediction, e.g., an interval, that provably contains the true target with any desired coverage level. Experimental results confirm the theoretical calibration guarantees of the proposed framework, referred to as quantum conformal prediction." @default.
- W4363671703 created "2023-04-11" @default.
- W4363671703 creator A5017736224 @default.
- W4363671703 creator A5087023232 @default.
- W4363671703 date "2023-04-06" @default.
- W4363671703 modified "2023-10-18" @default.
- W4363671703 title "Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning" @default.
- W4363671703 doi "https://doi.org/10.48550/arxiv.2304.03398" @default.
- W4363671703 hasPublicationYear "2023" @default.
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