Efficient Quantum Error Correction Analysis for Realistic Physical Error Models

              Quantum processors experience a vast array of errors beyond the stochastic Pauli errors typically used in the analysis of fault-tolerant quantum error correction (QEC). In this talk, I will introduce efficient methods to determine the performance of QEC for error models with stochastic, coherent, and time-dependent errors. I will show how to turn error models for physical qubits into detector error models (DEMs) that approximately produce the same circuit outcomes. These DEMs are efficient to sample from, enabling efficient approximate simulation of fault-tolerant gadgets with hardware-realistic noise. They are also interpretable, revealing the relative impacts of different physical level error on syndrome statistics and logical error rate, thereby enabling targeted hardware improvements. I will show how these methods enable principled analysis, and potentially improvement, of syndrome extraction and magic state cultivation performance in practice, based on fine-grained models for physical gate error.