Neural network approach to mitigating intra-gate crosstalk in superconducting CZ gates

TL;DR

This paper introduces a physics-guided neural control framework that enhances the fidelity and robustness of superconducting CZ gates by mitigating intra-gate crosstalk through optimized control pulses.

Contribution

It presents a novel neural control method that incorporates hardware and Hamiltonian knowledge to generate smooth, physically feasible pulses for crosstalk mitigation in superconducting qubits.

Findings

Numerical simulations show improved gate fidelity over baseline methods.

The approach yields smoother control pulses with better robustness.

Significant gains in worst-case fidelity under perturbed conditions.

Abstract

The potential of quantum computing is fundamentally constrained by the inherent susceptibility of qubits to noise and crosstalk, particularly during multi-qubit gate operations. Existing strategies, such as hardware isolation and dynamical decoupling, face limitations in scalability, experimental feasibility, and robustness against complex noise sources. In this manuscript, we propose a physics-guided neural control (PGNC) framework to generate robust control pulses for superconducting transmon qubit systems, specifically targeting crosstalk mitigation. By combining a hardware aware parameterization with a Hamiltonian-informed objective that accounts for condition-dependent crosstalk distortions, PGNC steers the search toward smooth and physically realizable pulses while efficiently exploring high dimensional control landscapes. Numerical simulations for the CZ gate demonstrate superior fidelity and pulse smoothness compared to a Krotov baseline under matched constraints. Taken together, the results show consistent and practically meaningful improvements in both nominal and perturbed conditions, with pronounced gains in worst-case fidelity, supporting PGNC as a viable route to robust control on near-term transmon devices.