Neural-network-based design and implementation of fast and robust quantum gates

Artikel i vetenskaplig tidskrift, 2026

We present a continuous-time, neural-network-based approach to optimal control in quantum systems, with a focus on pulse engineering for quantum gates. Leveraging the framework of neural ordinary differential equations (neural ODEs), we construct control fields as outputs of trainable neural networks, thereby eliminating the need for discrete parametrization or predefined bases. This allows for the generation of smooth, control-hardware-agnostic pulses that can be optimized directly using differentiable integrators. As a case study, we design and experimentally implement a short and detuning-robust π/2 pulse for photon number parity measurements in superconducting transmon circuits. This is achieved through simultaneous optimization for robustness and suppressing the leakage outside of the computational basis. These pulses have very high fidelity (≈99.95%) and realize the parity measurement with an efficiency greater than 99.9% over a detuning range of ≈± 20 MHz, thereby outperforming traditional techniques while retaining comparable gate durations. This showcases the potential of neural ODEs for high-performance quantum control in experimentally relevant settings.