  In the evolving landscape of quantum computing, determining the most
efficient parameters for Quantum Error Correction (QEC) is paramount. Various
quantum computers possess varied types and amounts of physical noise.
Traditionally, simulators operate in a forward paradigm, taking parameters such
as distance, rounds, and physical error to output a logical error rate.
However, usage of maximum distance and rounds of the surface code might waste
resources. An approach that relies on trial and error to fine-tune QEC code
parameters using simulation tools like STIM can be exceedingly time-consuming.
Additionally, daily fluctuations in quantum error rates can alter the ideal QEC
settings needed. As a result, there is a crucial need for an automated solution
that can rapidly determine the appropriate QEC parameters tailored to the
current conditions. To bridge this gap, we present MITS, a tool designed to
reverse-engineer the well-known simulator STIM for designing QEC codes. MITS
accepts the specific noise model of a quantum computer and a target logical
error rate as input and outputs the optimal surface code rounds and code
distances. This guarantees minimal qubit and gate usage, harmonizing the
desired logical error rate with the existing hardware limitations on qubit
numbers and gate fidelity. We explored and compared multiple heuristics and
machine learning models for training/designing MITS and concluded that XGBoost
and Random Forest regression were most effective, with Pearson correlation
coefficients of 0.98 and 0.96 respectively.
