Quantum Noise Spectroscopy via Randomized Benchmarking

Characterizing noise in quantum systems is a crucial aspect of diagnosing errors, designing targeted error protection protocols, and achieving reliable quantum computation. Two powerful methods for characterizing different aspects of quantum systems and operations are: Randomized Benchmarking (RB) and Quantum Noise Spectroscopy (QNS).

RB is a well-established technique used to spot errors in quantum operations that result from the noise. On the other hand, QNS focuses on characterizing time-correlated noise, providing unique insights into the noise encountered by quantum systems.

Here, we investigate the complementary strengths of RB and QNS and explore approaches for combining these methods to exploit their collective power. Specifically, we leverage the filter function formalism and the fluctuations in RB due to time-correlated noise to perform parametric estimation of the power spectral density.

Our analysis covers a wide range of noise spectra and compares the effectiveness of this combined approach to canonical QNS protocols.