Deep-learned error mitigation via partially knitted circuits for the variational quantum eigensolver

• URL: http://arxiv.org/abs/2506.04146v1
• Date: Wed, 04 Jun 2025 16:40:18 GMT
• Title: Deep-learned error mitigation via partially knitted circuits for the variational quantum eigensolver
• Authors: Simone Cantori, Andrea Mari, David Vitali, Sebastiano Pilati,
• Abstract summary: variational quantum eigensolver (VQE) is generally regarded as a promising quantum algorithm for near-term noisy quantum computers.<n>We show how to make VQE functional via a tailored error mitigation technique based on deep learning.
• Score: 0.4999814847776097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The variational quantum eigensolver (VQE) is generally regarded as a promising quantum algorithm for near-term noisy quantum computers. However, when implemented with the deep circuits that are in principle required for achieving a satisfactory accuracy, the algorithm is strongly limited by noise. Here, we show how to make VQE functional via a tailored error mitigation technique based on deep learning. Our method employs multilayer perceptrons trained on the fly to predict ideal expectation values from noisy outputs combined with circuit descriptors. Importantly, a circuit knitting technique with partial knitting is adopted to substantially reduce the classical computational cost of creating the training data. We also show that other popular general-purpose quantum error mitigation techniques do not reach comparable accuracies. Our findings highlight the power of deep-learned quantum error mitigation methods tailored to specific circuit families, and of the combined use of variational quantum algorithms and classical deep learning.

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