Speaker
Simone Bordoni
(Sapienza universita di Roma)
Description
This work presents a novel machine learning approach to characterize the noise impacting a quantum chip and emulate it during simulations. By leveraging reinforcement learning, we train an agent to introduce noise channels that accurately mimic specific noise patterns. The proposed noise characterization method has been tested on simulations for small quantum circuits, where it con- sistently outperformed randomized benchmarking, a widely used noise characterization technique. Furthermore, we show a practical application of the algorithm using the well-known Grover’s circuit.
| Sessione | Altro |
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Primary authors
Alejandro Sopena
(Instituto de F ́ısica Teo ́rica, UAM-CSIC, Universidad Aut ́onoma de Madrid, Cantoblanco, Madrid, Spain)
Andrea Papaluca
(Quantum Research Centre, Technology Innovation Institute, Abu Dhabi, UAE)
Piergiorgio Buttarini
(Quantum Research Centre, Technology Innovation Institute, Abu Dhabi, UAE.)
Simone Bordoni
(Sapienza universita di Roma)
Stefano Carrazza
(Istituto Nazionale di Fisica Nucleare)
Stefano Giagu
(Sapienza Università di Roma and Istituto Nazionale di Fisica Nucleare)
