Speaker
Nikita Korolev
(Russian Quantum Center)
Description
We investigate a machine learning-based quantum error mitigation method for the VQE algorithm. A training sample of near-Clifford schemes with classical simulation of noise-free observers is used. A comparison of regression models under various types of noise is conducted. It is shown that Ridge regression reduces the energy error and, at elevated noise levels, outperforms Zero Noise Extrapolation in terms of error suppression quality.
| Topic | Machine learning techniques for experimental quantum information science |
|---|---|
| Additional Topic | Hybrid quantum-classical approaches for learning and optimization |
Authors
Daniil Rabinovich
(Russian Quantum Center)
Kirill Lakhmansky
(Russian Quantum Center)
Nikita Korolev
(Russian Quantum Center)