ML Assisted Superconducting Qubit Readout
Abstract
In this work, we use a new approach based on neural networks. A neural network classifier can efficiently converge to the optimal joint filter with sufficient training. To discriminate qubit states, the neural network is trained in a supervised fashion with labeled, unfiltered measurement data recorded over several microseconds for each qubit configuration. In this work, the neural networks will be evaluated based on the following criteria: i. Classification inaccuracy ii. Classification inaccuracy with increasing number of qubits iii. Required measurement time to achieve optimal classification inaccuracy iv. Difference between training and test classification inaccuracy v. Extensiveness of neural network training to achieve optimal classification inaccuracy vi. Classification inaccuracy fluctuations due to changing experimental conditions This work could potentially lead to general, neuromorphic-based techniques for readout of quantum systems and, as such, may have great impact practical implementations of quantum information processors.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 08, 2020
- Source ID
- HR00112010001
Entities
People
- William D Oliver
Organizations
- Defense Advanced Research Projects Agency
- Massachusetts Institute of Technology