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تسجيل مستخدم جديدDirect Fidelity Estimation of Quantum States using Machine Learning
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In almost all quantum applications, one of the key steps is to verify that the fidelity of the prepared quantum state meets the expectations. In this paper, we propose a new approach to solve this problem using machine learning techniques. Compared to other fidelity estimation methods, our method is applicable to arbitrary quantum states, the number of required measurement settings is small, and this number does not increase with the size of the system. For example, for a general five-qubit quantum state, only four measurement settings are required to predict its fidelity with $pm1%$ precision in a non-adversarial scenario. This machine learning-based approach for estimating quantum state fidelity has the potential to be widely used in the field of quantum information.
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