/Denoising Qubit Calibration Data With Deep Learning
Abstract

Systems and methods for calibrating a qubit parameter for a qubit in a quantum computing system are provided. In one example, a method includes obtaining, by one or more computing devices, data associated with a set of one or more qubit parameters for a qubit in a quantum computing system. The method includes obtaining, by the one or more computing devices, calibration data associated with at least one qubit parameter in the set of one or more qubit parameters. The method includes determining, by the one or more computing devices, a value for the at least one qubit parameter based at least in part on the calibration data using a de-corrupting autoencoder.

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What is claimed is:

Systems and methods for calibrating a qubit parameter for a qubit in a quantum computing system are provided. In one example, a method includes obtaining, by one or more computing devices, data associated with a set of one or more qubit parameters for a qubit in a quantum computing system. The method includes obtaining, by the one or more computing devices, calibration data associated with at least one qubit parameter in the set of one or more qubit parameters. The method includes determining, by the one or more computing devices, a value for the at least one qubit parameter based at least in part on the calibration data using a de-corrupting autoencoder.
Timeline
Filed
06/02/2026
Published
09/24/2026
Granted
Not Available
IPC Codes(3)
G06N 10/60:Quantum algorithms, e.g. based on quantum optimisation, or quantum Fourier or Hadamard transforms
G06N 3/045:Combinations of networks
G06N 3/088:Non-supervised learning, e.g. competitive learning