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Because real-world decision-making environments are getting more complicated and changing all the time, we need reinforcement learning (RL) frameworks that are accurate, flexible, fast, and easy to understand. Even though they work, traditional RL methods often have problems with adaptability, stability, and exploration efficiency in environments that are random or not stationary. This paper presents a quantum reinforcement learning (QRL) framework that makes use of the parallelism and entanglement that are built into quantum computation to get around these problems. When compared, the QRL model does much better than the old method on six key performance indicators. The QRL model, for example, shows a 31.5% increase in exploration efficiency, a 50.7% decrease in reward variance, and a 2.44 \(\times \) increase in decision boundary complexity. It also makes things 9.1% more adaptable in changing situations, 27.6% better at finding solutions, and 16.1% faster at coming to conclusions. The quantum model also works well in a lot of different situations and makes it easier to understand policies. These results show that the QRL framework is a better and more useful alternative to high-fidelity decision-making when things get tough in the real world.

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Quantum Reinforcement Learning Framework for Agricultural Seed Treatment Optimization and Yield Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • K. Tamilarasi,
  • Isshaan Singh,
  • Divyansh Chawla,
  • Raghav Jain

摘要

Because real-world decision-making environments are getting more complicated and changing all the time, we need reinforcement learning (RL) frameworks that are accurate, flexible, fast, and easy to understand. Even though they work, traditional RL methods often have problems with adaptability, stability, and exploration efficiency in environments that are random or not stationary. This paper presents a quantum reinforcement learning (QRL) framework that makes use of the parallelism and entanglement that are built into quantum computation to get around these problems. When compared, the QRL model does much better than the old method on six key performance indicators. The QRL model, for example, shows a 31.5% increase in exploration efficiency, a 50.7% decrease in reward variance, and a 2.44 \(\times \) increase in decision boundary complexity. It also makes things 9.1% more adaptable in changing situations, 27.6% better at finding solutions, and 16.1% faster at coming to conclusions. The quantum model also works well in a lot of different situations and makes it easier to understand policies. These results show that the QRL framework is a better and more useful alternative to high-fidelity decision-making when things get tough in the real world.