Automated signal-to-noise ratio-based optimization of traveling-wave parametric amplifiers for multiplexed qubit readout.

Scaling superconducting quantum processors to hundreds of qubits requires frequency-multiplexed readout architectures, where the non-uniform gain profile of Traveling-Wave Parametric Amplifiers (TWPAs) creates a critical bottleneck. We present an automated optimization framework that addresses the weakest link problem in multiplexed readout by employing a geometric mean cost function to maintain a uniform signal-to-noise ratio (SNR) profile across all channels. Through systematic characterization of a 5-qubit system, we demonstrate that readout fidelity saturates above SNR ≈ 2.5, indicating that in the high-performance regime SNR is a more effective optimization metric for TWPAs than readout fidelity. Our optimization framework, based on the Nelder-Mead simplex algorithm with continuous parameter tuning, reaches the optimized operating point in ∼100 measurements, reducing the search effort by a factor of 4.5 compared to a 451-point exhaustive grid method. Notably, the continuous optimization surpasses the grid search maximum by 12.8% in geometric mean SNR, confirming its superior efficiency over conventional grid search methods. This automated approach provides a scalable solution for maintaining a uniformly high level of readout fidelity across various amplifier devices in large-scale quantum processors.