## 3 Key Findings
### What Security Enhancement Could the Quantum Mechanism Offer the IIoT Ecosystem?
#### 3.3.3 Quantum Deployment in IIoT
Thus, the cause of such vulnerable data is assumed to a compromised data or ill-processed data for decision-making. Meanwhile, smart industries rely on data analysis to optimize production, make predictions, preempt risk, and manage threats if they eventually occur. With this discovery, an attempt was made by the authors in [4] to identify hidden vulnerabilities in the smart industry settings via data classification with the aid of QNN to have secure and reliable big data decision-making in the IIoT ecosystems. The researchers, thus, implemented a quantum approximate optimization algorithm (QAOA) for the production optimization in logistics and product shipping across the four divisional layers of the IIoT system, namely the device, edge, fog, and cloud layers. However, the study did not provide a practical scenario to visualize the results of hands-on on quantum computers, though the simulation was run using IBM Quantum Lab's Qiskit software v.0.19.0. Conclusively, the trend at which quantum techniques have been deployed for IIoTsec in recent years is presented in Table 7.  
Is Hybrid Quantum-Classical More Efficient Against IIoT Security Challenges than a Single Technique Deployment? In Section 3.2, we discussed how learning techniques and blockchain have leveraged security enhancement to IIoT. At the same time, we discovered that DL algorithms have subtle records of deployment to IIoTsec, as presented in Table 7. Likewise, Section 3.3 revealed the esteemed value of quantum deployment to IIoTsec in the literature. Meanwhile, in both sections, we highlighted the limitations of each technique in their deployment accordingly. In this subsection, we aim to address the remnant security deficit in the IIoT should a hybrid of quantum principles and learning techniques be deployed. Our aim is inspired by the findings of [89; 109]. [PERSON] et al. [109] emphasized the security strength of hybridization of techniques in terms of being exponentially faster and computationally efficient for complex multidimensional functions than a classical algorithm for the same function. In addition, [PERSON] et al. [89] pointed out the advantages of hybrid quantum-classical, in particular, as it offers immediate detection and resolution of any security issue through its features of tight protections, comprehensive testing, and regular monitoring. Unfortunately, we discovered through our QAA that the quantum-classical approach is still at the infant stage owing to the state of access, technical know-how, and cost of quantum hardware. And even when quantum access is available, mostly via simulation, many works on quantum-classical approaches are still at the theoretical stage. Quantum-classical learning (QCL) is a novel and emerging subfield of quantum computing that uses quantum algorithms to solve problems in classical learning--ML and DL while speeding up the process exponentially [112]. A heterogeneous environment, like IIoT, has been characterized by several security breaches by adversaries; hence, it necessitates a blink-of-eye security approach. QCL has been identified for such an approach as it combines the inherent security properties of quantum with classical algorithms [36; 113]. In QCL, a classical dataset is often encrypted for use in quantum information processing,and the quantum result is only obtained when the state of such a quantum system is measured. A quantum state, unlike classical, are quantum bits (qubits) that communicate with each other at a faster speed than the speed of light, regardless of the distance apart. This feature also enhances qubit storage capability. For instance, a classical computer, using classical bits, could only store one of four possible binary configurations (00, 01, 10, or 11) at any given time, unlike a 2-qubit registry that is capable of simultaneously storing all four qubits because a qubit carries two integers. Thus, the increased capacity of qubits is proportional to the square number of qubits [114].