Quantum noise modeling through Reinforcement Learning

• URL: http://arxiv.org/abs/2408.01506v2
• Date: Tue, 26 Nov 2024 20:49:58 GMT
• Title: Quantum noise modeling through Reinforcement Learning
• Authors: Simone Bordoni, Andrea Papaluca, Piergiorgio Buttarini, Alejandro Sopena, Stefano Giagu, Stefano Carrazza,
• Abstract summary: We introduce a machine learning approach to characterize the noise impacting a quantum chip and emulate it during simulations. Our algorithm leverages reinforcement learning, offering increased flexibility in reproducing various noise models. The effectiveness of the RL agent has been validated through simulations and testing on real superconducting qubits.
• Score: 38.47830254923108
• License:
• Abstract: In the current era of quantum computing, robust and efficient tools are essential to bridge the gap between simulations and quantum hardware execution. In this work, we introduce a machine learning approach to characterize the noise impacting a quantum chip and emulate it during simulations. Our algorithm leverages reinforcement learning, offering increased flexibility in reproducing various noise models compared to conventional techniques such as randomized benchmarking or heuristic noise models. The effectiveness of the RL agent has been validated through simulations and testing on real superconducting qubits. Additionally, we provide practical use-case examples for the study of renowned quantum algorithms.

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