## 5 ML Intersection with 6G Enabling Technologies
### ML and QC Convergence for Optimal 6G Network  
Quantum computing and machine learning convergence plays a critical role in the provision of optimal applications in communication networks [19; 4]. The effect of QC combined with ML is compared with traditional reinforcement learning, random assignment reinforcement learning, and random dynamic channel assignment algorithms for the spectrum assignment of wireless communication networks [195]. The results show that QML achieved over a 40% increase in the speed of learning convergence when compared to traditional reinforcement learning.  
[PERSON] et al. [196] investigated the impact of ML integration with quantum key distribution (QKD) for attack detection in beyond 5G networks. Two ML approaches: artificial neural network (ANN) and Long Short-Term Memory (LSTM) techniques for detecting an attacker between a transmitter and receiver were proposed. The results revealed that ML integration enabled the detection of attackers to an accuracy of 99%. Although the attackers can operate at QC capacity, ML ensured that the presence of an attacker was detected during QKD without interfering with the key distribution process.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref** & **Application** & **ML Model** & **Outcome** \\ \hline
[185] & Resource allocation & DCO-OFDM & The developed model showed superior performance over Round Robin, Best CQI, and Genetic algorithms. \\ \hline
[186] & Adaptive handover mechanism & DRL & Optimized the AP selection strategy to maximize the downlink data rate. \\ \hline
[193] & Power allocation & DDPG & The developed DQN approach adapted well against fluctuations in user behavior in the network and achieved better convergence time compared to the QL-based approach. \\ \hline
[187] & Positioning optimization & Wk-NN & The proposed method achieved better positioning accuracy and outperformed all the conventional methods. \\ \hline
[188] & Improved indoor positioning system & SVM, DT, RF, and k-NN & The integrated approach required 78.26% less time and improved the positioning accuracy by 52.55%. The SVM method outperformed all the other approaches. \\ \hline
[189] & Indoor positioning system & 2 nd order regression, the polynomial trilateral & The integration yielded a position error within 4 cm–5 cm, which is better compared to when ML was not applied. \\ \hline
[190] & Signal detection & LSTM & LSTM and CNN approaches outperformed all the conventional LS methods for signal detection in all the scenarios considered. \\ \hline
[191] & Augmented spatial modulation & SVM, LR, and NN & ML integration enhanced the identification of 1000 transmitters with an accuracy above 99%. \\ \hline
[192] & Optimal data transmission routes & DRL & The proposed approach achieved optimal data transmission routes, lower delays, and high capacities. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Summary of ML integration in VLC.  
FL has also been integrated into QC for the privacy preservation of sensitive data [197]. Other applications that involve integrating ML with QC include network security, signal stability, parameter protocol selection, and coherent signal recognition [198; 199; 200; 201; 202; 203]. These studies all proved that ML plays a vital role in overall network optimization. A summary of the different applications is presented in Table 10 below.