Yulong Dong: Noise Learning with Quantum Signal Processing for Analog Quantum Computation
0Analog quantum computation is generally better suited for managing larger system sizes and longer simulation times than digital quantum computation. However, the lack of well-developed error characterization and correction techniques for analog systems significantly impedes it's practical applications. This gap highlights the critical need for developing specialized noise learning and calibration methods tailored to analog quantum computing. In this talk, we will introduce a metrology protocol designed specifically for estimating errors in control signals in analog quantum computers, which are subjected to continuous underlying dynamics. Furthermore, we will explore benchmarking methods that enhance system-level analysis by interleaving the evolution of the analog system with a control-feedforward ancilla qubit. We will discuss the advantages of this benchmarking method over previous approaches and how it opens up interesting research questions, such as the development of optimality analysis for analog metrology protocols. These findings will help guide and advance the development of error mitigation strategies in analog quantum computers.