Accuracy in IoT data acquisition has been an indispensable need to meet the increasing demand for time-sensitive data analysis, and real-time decision-making. Conspicuously, this study proposes a Quantum Computing inspired technique of temporal space optimization for real-time big IoT applications. For this purpose, quantification of IoT sensors is performed in terms of Sensors of Interest (SoI) and Degree of Aptness (DoA) measure to minimize IoT sensor-space in real-time. The proposed methodology incorporates quantum computing-based formalization of IoT sensor parameters to present a Quantum-Temporal Minimization Algorithm. Moreover, 2 key performance indicators in terms of Data Similarity Analysis and Energy Efficiency are estimated for optimized efficacy. To evaluate the presented technique, numerous simulations are performed in real-time scenario of vehicular traffic determination over 1km of Regional National Highway using 70 WiSense nodes comprising of noise sensors, vibration sensors, and Raspberry Pi device. Acquired data comprising of 28,586 segments are stored in the Amazon EC2 cloud database for evaluation. The performance enhancement is estimated based on comparative analysis with several state-of-the-art optimization techniques. Results registered depict that significant improvements are registered for the presented technique in terms of temporal effectiveness, and performance parameters like Accuracy, Correlation Analysis, and Reliability.

满足对时间敏感型数据分析和实时决策的不断增长的需求，物联网数据采集的准确性已成为不可或缺的需求。值得注意的是，这项研究提出了一种量子计算启发式的时空优化技术，用于实时大型物联网应用。为此，根据感兴趣的传感器（SoI）和适应度（DoA）度量对IoT传感器进行量化，以实时最小化IoT传感器空间。所提出的方法论结合了基于量子计算的物联网传感器参数的形式化，从而提出了一种量子时间最小化算法。此外，根据数据相似性分析和能源效率评估了2个关键性能指标，以优化功效。为了评估提出的技术，使用70个由噪声传感器，振动传感器和Raspberry Pi设备组成的WiSense节点，在1公里区域国家高速公路的实时交通流量确定场景中进行了许多模拟。包含28,586个段的获取数据存储在Amazon EC2云数据库中以进行评估。基于使用几种最先进的优化技术进行的比较分析，可以估计性能增强。记录的结果表明，就时间有效性和性能参数（如准确性，相关性分析和可靠性）而言，所提出的技术已取得重大改进。包含28,586个段的获取数据存储在Amazon EC2云数据库中以进行评估。基于使用几种最先进的优化技术进行的比较分析，可以估计性能增强。记录的结果表明，就时间有效性和性能参数（如准确性，相关性分析和可靠性）而言，所提出的技术已取得重大改进。包含28,586个段的获取数据存储在Amazon EC2云数据库中以进行评估。基于使用几种最先进的优化技术进行的比较分析，可以估计性能增强。记录的结果表明，就时间有效性和性能参数（如准确性，相关性分析和可靠性）而言，所提出的技术已取得重大改进。