WiMi Deploys Quantum Computing Optimization Based on Multi-Objective Deep Reinforcement Learning

▤ Summary

WiMi Hologram Cloud says it has deployed a quantum computing optimization approach that uses multi-objective deep reinforcement learning to improve quantum control. The method reuses single-process quantum control results to speed convergence and avoid redundant computation while optimizing multiple objectives such as gate fidelity, efficiency, noise suppression, and energy use. The company positions this data-driven technique as an improvement over traditional single-objective, model-driven control methods that can get stuck in local optima. WiMi says it will continue investing in interdisciplinary AI and quantum research to advance quantum computing and support industry transformations.

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