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

Marko Kuzmanović1, Ilya Moskalenko1, Yu-Han Chang1, Ognjen Stanisavljević1, Christopher Warren2, Emil Hogedal2, Anuj Aggarwal2, Irshad Ahmad2, Janka Biznárová2 et al.

Mamta Dahiya2, Marcus Rommel2, Andreas Nylander2, Giovanna Tancredi2, and Gheorghe Sorin Paraoanu1

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Phys. Rev. Applied 26, 034070 – Published 29 September, 2026

DOI: https://doi.org/10.1103/4zzl-qbsf

Abstract

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.

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Phys. Rev. Applied 26, 034070– Published 29 September, 2026
  • Received 17 July 2025
  • Revised 6 February 2026
  • Accepted 31 August 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

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