Noise-Tolerant Gradient Measurement of Matrix Norm for Programmable Unitary Converters
Yoshitaka Taguchi, Yasuyuki Ozeki · 2023
Programmable unitary converters are powerful tools for realizing unitary transformations, essential in the fields of quantum computing, machine learning, and optical communication. The precision of these unitary transformations is crucial for maintaining high fidelity in such applications. However, various physical artifacts can impair the accuracy of these transformations. A commonly employed approach utilizes the system's gradient to restore accuracy. Although this gradient can indeed be physically measured using external equipment, it leads to a rather bulky optical system. Alternatively, some studies propose approximating the gradient by finite difference, which demands precise parameter control and measurement. However, such precision leaves this approach vulnerable to noise. In this study, we propose a gradient measurement method that is resistant to noise and does not require any supplementary equipment. Our numerical analysis demonstrates that our method exhibits orders of magnitude higher tolerance to noise than the prior approach, thus considerably reducing the system requirements.