Neural nets design quantum gates with 99.95% accuracy

Marko Kuzmanović and colleagues have designed and experimentally implemented a quantum gate with 99.95% accuracy using a novel approach to pulse engineering. The researchers used neural ordinary differential equations to construct control fields as outputs of trainable neural networks, eliminating the need for predefined bases or discrete parametrization. This method achieved greater than 99.9% efficiency for photon number parity measurements in superconducting transmon circuits across a detuning range of approximately ±20MHz, outperforming traditional techniques while maintaining comparable gate durations. This work demonstrates the potential of neural ODEs for high-performance quantum control in experimentally relevant settings. By modeling the control field as a continuous function of time, the system optimizes quantum dynamics through differentiable simulation, a departure from conventional methods. Standard pulse designs, like Rabi or DRAG pulses, often struggle with noise, detuning and fluctuations in drive amplitude, requiring specific error modeling for operator cancellation. Neural…