Robust Parametric Quantum Gate Against Stochastic Time-Varying Noise

Abstract

The performance of quantum processors in the noisy intermediate-scale quantum (NISQ) era is severely constrained by environmental noise and other uncertainties. While the recently proposed quantum control robustness landscape (QCRL) offers a powerful framework for generating robust control pulses for parametric gate families, its application has been practically restricted to quasi-static noise. To address the spectrally complex, time-varying noise prevalent in reality, we propose filter function-enhanced QCRL (FF-QCRL), which integrates filter function formalism into the QCRL framework. The resulting FF-QCRL algorithm minimizes a generalized robustness metric that faithfully encodes the impact of stochastic processes, enabling robust pulse-family generation for parametric gates under realistic time-varying noise. Numerical validation in a representative single-qubit setting confirms the effectiveness of the proposed method.

Figures (7)

• Figure 1: The VQC surrogate architecture for RIPV. Pulse parameters are encoded into a variational quantum circuit and measured through an observable readout with classical post-processing. The resulting surrogate output is differentiated with respect to input parameters and used as an auxiliary update-direction signal in RIPV.
• Figure 2: Initialization results for the gate $R_x(\pi)$ under stochastic detuning noise. (a) Comparison of the waveforms between the initial guess (RCP (static)) and the optimized pulse (Proposed). (b) Filter functions on logarithmic scale with the noise PSD $S(\omega)$ (dashed line) and the gray-shaded regions indicating the targeted noise bands.
• Figure 3: Monte Carlo validation of the initialization for the $R_x(\pi)$ gate under stochastic detuning noise. (a) Average fidelity versus RMS noise strength; shaded regions indicate $\pm 1$ standard deviation. (b) Average infidelity on logarithmic scale.
• Figure 4: Control pulse parameters for the $R_x(\theta)$ gate as functions of rotation angle $\theta \in [\pi, 2\pi]$.
• Figure 5: Monte Carlo validation of the generation for the gates $R_x(\theta)$ under stochastic detuning noise. (a) Average fidelity comparison under stochastic detuning noise. (b) Average infidelity comparison on a logarithmic scale under stochastic detuning noise.