Quantum Gate Optimization via Reinforcement Learning with HyperScore-Guided Exploration in Trapped-Ion Systems
Description
Quantum Gate Optimization via Reinforcement Learning with HyperScore-Guided Exploration in Trapped-Ion Systems This work introduces a Reinforcement Learning (RL)–HyperScore framework for optimizing quantum gate control pulses in trapped-ion systems. Gate fidelity—the accuracy with which a quantum gate performs its intended operation—is central to fault-tolerant quantum computing, yet current optimization methods (e.g., gradient descent) often stall in high-dimensional spaces and are sensitive to noise. Approach: The method combines a hierarchical RL agent—with a meta-controller adjusting coarse parameters (pulse duration, amplitude) and a low-level controller refining pulse shapes—with a HyperScore function that guides exploration. The HyperScore integrates three metrics: Gate fidelity (0–1) Robustness to noise (fidelity preservation under simulated noise) Pulse energy efficiency Weights for these metrics are learned via Bayesian optimization, adapting to current conditions. The HyperScore is computed as: HS = 100 × [1 + (σ(β·ln(V) + γ))^κ], where β (sensitivity), γ (bias), and κ (power exponent) are dynamically tuned. This score shapes the RL agent’s Boltzmann exploration distribution, focusing search in high-potential parameter regions. Simulation Environment: An 89-level model of a ^171Yb^+ ion in a linear Paul trap was used, including realistic noise sources—spontaneous emission, magnetic field fluctuations, and laser frequency drift. 10,000 candidate control pulses were evaluated, each scored on fidelity, robustness, and efficiency. Results: For the Mølmer–Sørensen gate, the framework improved average fidelity by 15% over gradient descent (99.8% vs. 98.5%) and boosted robustness (98.5% vs. 95%). Energy efficiency improved by ~8% through elimination of unnecessary pulse components. Advantages: Dynamically balances exploration and exploitation Adapts to environmental noise Scalable to larger systems via hierarchical control Future Work: Extending to multi-qubit gates, hardware-in-the-loop integration, and meta-learning of the HyperScore function. Impact: By combining adaptive RL with HyperScore-guided exploration, this framework overcomes common pitfalls of static optimization, offering a scalable, noise-resilient pathway toward high-fidelity trapped-ion quantum computing. This document is a part of the Freederia Research Archive. Explore our complete collection of advanced research at [en.freederia.com](https://en.freederia.com), or visit our main portal at [freederia.com](https://freederia.com) to learn more about our mission and other initiatives.