WiMi Studies Quantum Encoding Circuit Adaptation Optimization Architecture Based on Reinforcement Learning

WiMi Hologram Cloud Inc., a leading global Hologram Augmented Reality (“AR”) Technology provider, leverages the interdisciplinary technology of quantum computing and machine learning to propose a specific problem encoding circuit generation scheme based on reinforcement learning technology, breaking the limitations of traditional heuristic design and providing an innovative technical path for improving the performance of QML models. This scheme takes task adaptability optimization as its core objective and utilizes the intelligent search capability of reinforcement learning algorithms to achieve automated and customized generation of encoding circuit architectures, which will solve the core problems of traditional design methods, such as poor adaptability, low search efficiency, and insufficient consideration of multiple objectives.

WiMi’s proposed technical solution has its core innovations concentrated on the deep integration of model-based reinforcement learning algorithms and hierarchical circuit structures, constructing a sample-efficient encoding circuit search framework. Unlike traditional reinforcement learning algorithms, this solution adopts a model-based reinforcement learning strategy. By constructing an environment model to predict the performance of encoding circuits, it eliminates the need to perform actual quantum hardware evaluations for every candidate circuit architecture, significantly reducing the number of necessary circuit evaluations during the search process, effectively lowering quantum resource consumption, and markedly improving search efficiency. This sample-efficient characteristic enables it to rapidly explore the optimal encoding circuit architecture adapted to specific tasks under limited quantum computing resource constraints.

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The introduction of the hierarchical circuit structure further breaks through the performance bottlenecks of traditional search algorithms. It abandons the traditional algorithm’s blind search mode for complete circuit architectures by decomposing the encoding circuit into multiple levels of basic modules. Through hierarchical exploration and combinatorial optimization of each level of modules by the reinforcement learning agent, a complete encoding circuit adapted to specific tasks is gradually constructed. This hierarchical design approach effectively decomposes the originally huge circuit search space, substantially reducing the search dimensions, avoiding efficiency losses caused by redundant searches, and ensuring through module-level optimization that the final generated encoding circuit possesses better structural rationality and performance stability. Compared with traditional search algorithms, this hierarchical strategy achieves dual improvements in search efficiency and architecture optimization effectiveness, providing efficient and feasible technical support for the customized generation of encoding circuits.

WiMi’s researched technical solution possesses strong multi-objective optimization capabilities, enabling it to fully meet the diversified needs of different application scenarios and precisely match the core requirements of tasks. During the encoding circuit generation process, it can simultaneously consider multiple core objectives such as model performance, quantum resource consumption, and noise robustness. Through the design of a multi-objective reward function, the reinforcement learning agent achieves synergistic optimization of various objectives during the search process, avoiding performance imbalance issues caused by single-objective optimization. In the future, WiMi will continue to deeply explore the interdisciplinary field of quantum computing and machine learning technology. Relying on its own technological accumulation and innovation capabilities, it will continuously optimize the encoding circuit generation solution, lead the sustained development of QML technology, and contribute to the progress of the global quantum computing industry.