Direct Fidelity Estimation of Quantum States using Machine Learning

Abstract

In almost all quantum applications, one of the key steps is to verify that the fidelity of the prepared quantum state meets expectations. In this Letter, we propose a new approach solving 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 $\pm 1\%$ precision in a nonadversarial scenario. This machine-learning-based approach for estimating quantum state fidelity has the potential to be widely used in the field of quantum information.

arXiv comment20 pages, 10 figures

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• Quantum Similarity Testing with Convolutional Neural Networks
• Noise-Agnostic Quantum Error Mitigation with Data Augmented Neural Models
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• Operation fidelity explored by numerical range of Kraus operators
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• Q-gen: A Parameterized Quantum Circuit Generator
• Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
• Scalable Experimental Bounds for Entangled Quantum State Fidelities
• Active Learning for Quantum Mechanical Measurements
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• Constrained Shadow Tomography for Molecular Simulation on Quantum Devices