Enhancing Qubit Readout with Autoencoders

• URL: http://arxiv.org/abs/2212.00080v2
• Date: Wed, 6 Sep 2023 12:20:40 GMT
• Title: Enhancing Qubit Readout with Autoencoders
• Authors: Piero Luchi, Paolo E. Trevisanutto, Alessandro Roggero, Jonathan L. DuBois, Yaniv J. Rosen, Francesco Turro, Valentina Amitrano, Francesco Pederiva
• Abstract summary: This work proposes a novel readout classification method for superconducting qubits based on a neural network pre-trained with an autoencoder approach. We demonstrate that this method can enhance classification performance, particularly for short and long time measurements.
• Score: 36.136619420474766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In addition to the need for stable and precisely controllable qubits, quantum computers take advantage of good readout schemes. Superconducting qubit states can be inferred from the readout signal transmitted through a dispersively coupled resonator. This work proposes a novel readout classification method for superconducting qubits based on a neural network pre-trained with an autoencoder approach. A neural network is pre-trained with qubit readout signals as autoencoders in order to extract relevant features from the data set. Afterwards, the pre-trained network inner layer values are used to perform a classification of the inputs in a supervised manner. We demonstrate that this method can enhance classification performance, particularly for short and long time measurements where more traditional methods present lower performance.

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