Neural Sensing with Superconducting Oscillator Networks

Abstract

We propose using superconducting nonlinear oscillators (SNAILs) to amplify weak signals from quantum systems, leveraging their inherent nonlinearity for reservoir computing. By modeling the SNAIL system as a neural network, we aim to improve qubit state discrimination through training, thereby enhancing the accuracy of quantum measurements. Our current work involves simulating a nonlinear system composed of a SNAIL driven by an optical pump. We generate time-series data for both the signal and idler modes, which are then used to train and perform weighted binary classification of values obtained from the SNAIL system under varying noise distributions with the same mean. Two nonlinearities are present within the SNAIL system: the third-order nonlinearity (g3), which is primarily responsible for the amplification process, and the fourth-order non-linearity (g4), or Kerr nonlinearity, which we examine as a basis for reservoir computing and classification. We seek to identify the optimal regime for classification by varying g4 and the phase of the pump. Upon examination, we found no consistent regime with significantly improved classification performance; however, certain combinations of g4 and pump phase showed marginal gains, suggesting potential for optimization in more targeted parameter spaces.