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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