  This research addresses the critical necessity for advanced rapid response
operations in managing a spectrum of environmental hazards. We propose a novel
framework, qIoV that integrates quantum computing with the Internet-of-Vehicles
(IoV) to leverage the computational efficiency, parallelism, and entanglement
properties of quantum mechanics. Our approach involves the use of environmental
sensors mounted on vehicles for precise air quality assessment. These sensors
are designed to be highly sensitive and accurate, leveraging the principles of
quantum mechanics to detect and measure environmental parameters. A salient
feature of our proposal is the Quantum Mesh Network Fabric (QMF), a system
designed to dynamically adjust the quantum network topology in accordance with
vehicular movements. This capability is critical to maintaining the integrity
of quantum states against environmental and vehicular disturbances, thereby
ensuring reliable data transmission and processing. Moreover, our methodology
is further augmented by the incorporation of a variational quantum classifier
(VQC) with advanced quantum entanglement techniques. This integration offers a
significant reduction in latency for hazard alert transmission, thus enabling
expedited communication of crucial data to emergency response teams and the
public. Our study on the IBM OpenQSAM 3 platform, utilizing a 127 Qubit system,
revealed significant advancements in pair plot analysis, achieving over 90% in
precision, recall, and F1-Score metrics and an 83% increase in the speed of
toxic gas detection compared to conventional methods.Additionally, theoretical
analyses validate the efficiency of quantum rotation, teleportation protocols,
and the fidelity of quantum entanglement, further underscoring the potential of
quantum computing in enhancing analytical performance.
