## 5 Conclusions  
This paper provided a new approach to task scheduling for heterogeneous signal processing platforms. The main technology of this paper was the adaptation of quantum genetic algorithms to task scheduling on heterogeneous processing platforms by improving traditional genetic algorithms through quantum computing. These included qubit  
Figure 12: Effect of different CCRs on speedup.  
Figure 13: Scheduling efficiency of different processing systems.  
coding to address the lack of population diversity, full-interference crossover operations to address the problem of easily falling into local optima, and quantum rotation gates to address the problem of slow convergence. From the analysis, it could be concluded that the proposed algorithm had an average reduction in the scheduling length of 14.5%, an average increase in the convergence speed of 1319.7%, and an average increase in the speedup of 24.2% compared to other algorithms. The algorithm improved the scheduling efficiency in heterogeneous systems by an average of 36.6% over homogeneous systems. Overall, the algorithm offered higher scheduling performance and scenario adaptation for computation-intensive tasks and heterogeneous systems. However, task scheduling is a NP-hard problem, and the hardware structure and signal resources of heterogeneous signal processing platforms are complex. The algorithm in this paper still has some shortcomings and needs to be improved in practical applications:  
1. When modeling the hardware structure, this paper set the communication transmission between heterogeneous processors as an ideal state, without considering the problem of communication competition. However, in the actual platform application, the transmission bandwidth between processors or boards is different, and the communication competition during data transmission may cause delays. The next step is to improve and perfect the DAG model and hardware architecture model.
2. The algorithm proposed in this paper used a large number of binary matrices in encoding and decoding, which increased the time complexity of the algorithm and the running time of the system. To solve this problem, we can map the processor scheduling policy to the vector set. In decoding, the quantum individual is mapped to a vector, thus a vector set is obtained, which corresponds to a processor scheduling strategy. Or we can use algorithms that specifically calculate matrix operations, such as the CORDIC (coordinated rotation digital computer) algorithm instead of quantum rotation gate operation.
3. The algorithm proposed in this paper was mainly aimed at computation-intensive task scheduling, but there are also communication-intensive tasks in signal processing. We can reduce the communication overhead by task clustering or task duplication.  
Conceptualization, Y.L.; data curation, X.S.; formal analysis, Z.X. and J.M.; supervision, K.Z.; project administration, Z.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used in this paper can be obtained by contacting the corresponding author.  
The authors declare no conflict of interest.