Quantum circuit fidelity estimation using machine learning

• URL: http://arxiv.org/abs/2212.00677v3
• Date: Mon, 19 Dec 2022 22:38:14 GMT
• Title: Quantum circuit fidelity estimation using machine learning
• Authors: Avi Vadali, Rutuja Kshirsagar, Prasanth Shyamsundar, Gabriel N. Perdue
• Abstract summary: We introduce a machine-learning-based technique to estimate the fidelity between the state produced by a noisy quantum circuit and the target state corresponding to ideal noise-free computation. We demonstrate that the trained model can predict the fidelities of more complicated circuits for which such methods are infeasible.
• Score: 0.4588028371034406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The computational power of real-world quantum computers is limited by errors. When using quantum computers to perform algorithms which cannot be efficiently simulated classically, it is important to quantify the accuracy with which the computation has been performed. In this work we introduce a machine-learning-based technique to estimate the fidelity between the state produced by a noisy quantum circuit and the target state corresponding to ideal noise-free computation. Our machine learning model is trained in a supervised manner, using smaller or simpler circuits for which the fidelity can be estimated using other techniques like direct fidelity estimation and quantum state tomography. We demonstrate that the trained model can predict the fidelities of more complicated circuits for which such methods are infeasible.

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