Adaptive AI for Pulse-Level Quantum Control

• URL: http://arxiv.org/abs/2609.08727v1
• Date: Tue, 08 Sep 2026 13:25:37 GMT
• Title: Adaptive AI for Pulse-Level Quantum Control
• Abstract summary: We show that a reinforcement-learning agent based on a deep Q learning network can autonomously discover high-performance pulse sequences.<n>Results highlight adaptive learning as a promising hardware-ready framework for pulse-level quantum control.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Control Variational Quantum Eigensolver (ctrl-VQE) directly optimizes microwave pulses to enable faster and lower-error quantum-state preparation, but its continuous control landscape re- quires efficient search strategies. We demonstrate that a reinforcement-learning agent based on a deep Q learning network can autonomously discover high-performance pulse sequences using only system parameters and a reward function. The approach is fully general for superconducting qubit platforms, requires no ansatz, and operates at nanosecond resolution compatible with hardware con- straints. As a proof of concept, we apply the method to ground-state preparation of the Hydrogen molecule on a simulated superconducting device. The agent consistently identifies optimized control sequences that achieve high fidelity and outperform random-search baselines. These results highlight adaptive learning as a promising hardware-ready framework for pulse-level quantum control.

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