# Quantum PSO-Based Optimization of Secured IRS-Assisted Wireless-Powered IoT Networks
### Related Work
#### 1.1.3 IRS-Assisted Secure Communication
The authors in [42] propose an IRS-aided secure WPCN with multiple eavesdroppers, where a transmitter harvests energy from a PS and uses it for secure multicast to IoT devices. The energy interaction between the PS and transmitter is modeled as a Stackelberg game, and an IRS-aided energy trading and secure communication (IRS-ETSC) scheme is developed. A two-step approach using alternating optimization and semidefinite relaxation is employed to optimize the key parameters, achieving significant utility improvements compared to non-IRS schemes. Furthermore, minimization of computational complexity and processing latency is paramount in architectural designs of network models [43]. To tackle the issues associated with computational complexity and resource allocation in wireless communications systems while simultaneously preserving privacy performance, the integration of AI-based methodologies with heuristic strategies has attracted considerable interest in both industry and scholarly research. For example, the researchers in [44] introduced a novel power allocation methodology for NOMA networks, utilizing particle swarm optimization to enhance secure computational efficiency and secrecy energy efficiency. PSO, a prominent AI technique, is frequently utilized to derive approximate solutions for complex optimization problems [45]. This algorithm is recognized for its robust optimization capabilities because it progressively refines candidate solutions by adjusting the positions and velocities of particles within a swarm. Nevertheless, PSO exhibits certain limitations, especially when the constraints of the problem escalate or when the intricacies of the network environment intensify. In such scenarios, the rate of convergence for PSO tends to diminish markedly, obstructing its capacity to efficiently attain the optimal solution. As a result, there is a need for alternative optimization methodologies that provide reduced computational complexity, possess the capability to manage intricate environments, and that guarantee substantial reliability in wireless communications systems. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Definition** & **Notation** \\ \hline Conjugate Transpose & Conjugate transpose of a complex vector/matrix & \((\mathbf{b})^{H}\) \\ Complex Matrix Space & Space of \(m\times n\) complex matrices & \(\mathbb{C}^{m\times n}\) \\ Imaginary Unit & Imaginary unit where \(i^{2}=-1\) & \(i\) \\ Absolute Value & Magnitude of a complex scalar \(\beta\) & \(|\beta|\) \\ Matrix Trace & Sum of diagonal elements of \(\mathbf{B}\) & \(\mathrm{Tr}(\mathbf{B})\) \\ Diagonal Matrix & Diagonal matrix formed from vector \(\mathbf{b}\) & \(\mathrm{diag}(\mathbf{b})\) \\ Rank of a Matrix & Number of linearly independent rows/columns in \(\mathbf{B}\) & \(\mathrm{rank}(\mathbf{B})\) \\ Positive Semidefinite & A matrix \(\mathbf{B}\) with all eigenvalues \(\geq 0\) & \(\mathbf{B}\succeq 0\) \\ Matrix Element & Element at \(m\)-th row, \(n\)-th column of \(\mathbf{B}\) & \([\mathbf{B}]_{m\times n}\) \\ Phase-Shift Matrix & IRS diagonal matrix with phase shift \(\theta_{n}\) & \(\mathbf{\Phi}=\mathrm{diag}(\mathbf{a}_{n}e^{\mathrm{i}\mathbf{a}_{n}})\) \\ Energy Harvesting & Efficiency of energy harvesting devices & \(\xi\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations used for mathematical expressions.