Quantum-Inspired Dynamic Decision-Making Algorithm (QIDDM): A Robust Framework for Delayed Commitment in Uncertain Environments

Abstract

This paper introduces the Quantum-Inspired Dynamic Decision-Making Algorithm (QIDDM), a novel framework that leverages quantum mechanical principles-superposition, entanglement, and collapse-to optimize decision-making in dynamic, uncertain environments. By maintaining a probabilistic superposition of potential actions until contextual data triggers a collapse, QIDDM delays premature commitments, enabling adaptive responses in robotics, finance, and reinforcement learning. Experimental validation in simulated robotic navigation and financial trading environments demonstrates a 27% improvement in decision accuracy and 33% reduction in premature commitments compared to classical threshold-based and reinforcement learning methods. The algorithm’s mathematical formalism, scalability, and real-world applicability are rigorously analyzed, establishing it as a transformative approach for dynamic decision-making.

Introduction

Decision-making under uncertainty remains a key challenge for autonomous systems. Traditional methods like greedy algorithms and MDPs often make suboptimal choices due to static rules or limited context. Inspired by quantum mechanics’ superposition principle, the proposed Quantum-Inspired Decision-Making (QIDDM) framework models decisions as probabilistic superpositions that collapse into specific actions only when enough information reduces uncertainty.

Key points:

• Model: Decisions exist in a superposition of multiple states with amplitudes adjusted dynamically based on real-time context (e.g., sensors or market data).

• Decision Collapse: A choice is made only when entropy (uncertainty) falls below a threshold, delaying commitment until confident.

• Algorithm: Efficient implementation with low complexity for updating states, calculating entropy, and making decisions.

• Validation: QIDDM outperforms Q-learning, Monte Carlo Tree Search, and threshold methods in robotic navigation (higher success, fewer premature decisions) and financial portfolio management (better returns and risk metrics).

• Advantages: Reduces errors in volatile environments and adapts better over time.

• Challenges & Future Work: Scaling to large state spaces, integrating deep learning, and deploying in real-world applications like self-driving cars and financial AI.

• Note: Although inspired by quantum concepts, QIDDM is a classical algorithm and does not require quantum hardware.

Conclusion

QIDDM bridges quantum-inspired principles with classical decision theory, offering a robust framework for dynamic environments. Its delayed commitment strategy, validated through simulations, demonstrates significant improvements over existing methods. Future work will integrate deep learning for amplitude adjustment and deploy QIDDM in real-world autonomous vehicles.

References

[1] Nielsen, M. A., & Chuang, I. L. (2010). Quantum Computation and Quantum Information. Cambridge University Press. [2] Sutton, R. S., &Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press. [3] Biamonte, J. et al. (2017). Quantum machine learning. Nature, 549(7671), 195-202.https://doi.org/10.1038/nature23474 [4] Orús, R. (2019). Quantum computing for finance: Overview and prospects. Reviews in Physics, 4, 100028. https://doi.org/10.1016/j.revip.2019.100028

Copyright

Copyright © 2025 Mr. Mahesh Y. Gondekar, Mr. Sanskar H. Goenka. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.