Quantum Gate Control Trajectories Using Reinforcement Learning

The reinforcement learning model performs preliminary training with stochastic noise incorporated into the training process. The agent learns optimal control trajectories by experiencing noisy environments during training, enabling it to produce noise-robust control sequences before actual quantum gate execution. This preliminary adaptation to noise conditions resolves the contradiction by preparing the control system to maintain fidelity despite hardware noise during actual computation.

The reinforcement learning framework implements continuous feedback through the reward function, which evaluates control trajectories based on gate fidelity, leakage errors, and runtime. The agent uses this feedback to iteratively improve its control policies, learning to balance fidelity requirements with computational efficiency. The feedback mechanism enables the system to automatically optimize control sequences that maintain high gate fidelity while minimizing runtime, resolving the contradiction between reliability and productivity.

The reinforcement learning model performs preliminary training with stochastic noise incorporated into the training process. The agent learns optimal control trajectories by experiencing noisy environments during training, enabling it to produce noise-robust control sequences before actual quantum gate execution. This preliminary adaptation to noise conditions resolves the contradiction by preparing the control system to maintain fidelity despite hardware noise during actual computation.

The training process incorporates variable noise parameters and reward function weights that can be adjusted to balance training comprehensiveness with training time. By carefully selecting noise levels and reward priorities, the system achieves sufficient robustness without requiring excessively long training periods. This parameter optimization resolves the contradiction between achieving noise robustness and minimizing training time investment.

The reinforcement learning agent learns universal control policies that can handle multiple objectives (minimizing leakage, reducing infidelity, optimizing runtime) through a single unified reward function. This universal approach replaces the need for separate control sequences for each objective, achieving high gate fidelity without proportionally increasing control sequence complexity. The multi-functionality of the learned policy resolves the contradiction by consolidating multiple fidelity-enhancing controls into a unified sequence.

The reward function uses adjustable penalty weights for leakage and infidelity that can be tuned to achieve desired fidelity levels without excessively complicating control sequences. By optimizing these parameter weights, the system achieves high gate fidelity with minimally complex control sequences, resolving the contradiction between fidelity enhancement and control complexity.