aiqbench analysis
This study demonstrates a hybrid framework combining optimal control theory with contextual deep reinforcement learning to calibrate high-fidelity gates in multi-level quantum systems. By treating RL as a calibration layer rather than a competing optimizer, the method effectively corrects for model mismatches and parameter fluctuations across different hardware ensembles. The work establishes RL as a scalable tool for enhancing the robustness of quantum operations in complex qudit architectures.
Reinforcement Learning for Robust Calibration of Multi-Qudit Quantum Gates
arXiv:2604.19990v2 Announce Type: replace Abstract: Higher-dimensional quantum systems, such as qudits, offer architectural and algorithmic advantages over qubits, but their increased spectral crowding and limited controllability render high-fidelity quantum gates particularly challenging. We propose a hybrid optimization framework that integrates optimal control theory methods with contextual deep reinforcement learning to achieve robust controlled-phase gates on two qutrits. Optimal control is first used to design high-fidelity control pulses for a nominal system model. Reinforcement learning is then employed as a calibration stage that learns small residual corrections to these pulses in the presence of static model mismatch, thereby preserving good gate performance under realistic parameter uncertainties. By learning structured, low-dimensional residual corrections conditioned on device-specific parameter variations, reinforcement learning enhances the transfer robustness of nominally optimal but parameter-sensitive control solutions across ensembles of devices. Crucially, the reinforcement learning step in our framework does not compete with the optimal control step but provides the adaptability required for realistic hardware, systematically reducing the sensitivity to parameter fluctuations. Our results establish reinforcement learning as a practical and scalable ingredient for robust calibration of quantum gates in high-dimensional systems.
Abstract:Higher-dimensional quantum systems, such as qudits, offer architectural and algorithmic advantages over qubits, but their increased spectral crowding and limited controllability render high-fidelity quantum gates particularly challenging. We propose a hybrid optimization framework that integrates optimal control theory methods with contextual deep reinforcement learning to achieve robust controlled-phase gates on two qutrits. Optimal control is first used to design high-fidelity control pulses for a nominal system model. Reinforcement learning is then employed as a calibration stage that learns small residual corrections to these pulses in the presence of static model mismatch, thereby preserving good gate performance under realistic parameter uncertainties. By learning structured, low-dimensional residual corrections conditioned on device-specific parameter variations, reinforcement learning enhances the transfer robustness of nominally optimal but parameter-sensitive control solutions across ensembles of devices. Crucially, the reinforcement learning step in our framework does not compete with the optimal control step but provides the adaptability required for realistic hardware, systematically reducing the sensitivity to parameter fluctuations. Our results establish reinforcement learning as a practical and scalable ingredient for robust calibration of quantum gates in high-dimensional systems.
arXiv:2604.19990 [quant-ph]
(or arXiv:2604.19990v2 [quant-ph] for this version)
Reinforcement learning for robust calibration of multiqudit quantum gates, Amine Jaouadi and Sahe Ashhab, Phys. Rev. A 114, 022463 Published 26 August, 2026
https://doi.org/10.1103/717q-ypkg DOI(s) linking to related resources
From: Amine Jaouadi Dr. [view email] [v1] Tue, 21 Apr 2026 21:00:27 UTC (1,861 KB) [v2] Wed, 26 Aug 2026 13:11:48 UTC (1,583 KB)
Full text under the publisher's open licence · 240 words
