This paper explores the application of transfer learning (TL) to optimize parameters for the Quantum Approximate Optimization Algorithm (QAOA) in solving combinatorial optimization problems (COPs). The authors investigate whether pre-optimized QAOA parameters from one problem instance can be effectively reused for different COP instances. They select six COPs: Traveling Salesman Problem (TSP), Bin Packing Problem (BPP), Knapsack Problem (KP), Weighted Maximum Cut (MaxCut), Maximal Independent Set (MIS), and Portfolio Optimization (PO). The study focuses on finding optimal $\beta$ and $\gamma$ parameters for $p$ layers of QAOA. The key findings are: 1. **BPP shows the best transferability**: Among all the COPs, BPP parameters transfer well to both larger instances of the same problem and other different problems. 2. **TL improves performance**: The probability of finding the optimal solution is above a quadratic speedup over random guessing for all problems, indicating good generalization capabilities. 3. **Real quantum hardware experiments**: The BPP parameters are tested on various quantum hardware devices, including Rigetti Aspen-M-3, IBM Brisbane, and IonQ Aria, for MIS problems with 8, 14, and 18 qubits. IonQ Aria shows the best overlap with the ideal probability distribution. 4. **Cross-platform TL**: The parameters from BPP are also transferred to a D-Wave Advantage quantum annealer, showing consistent improvement in the distribution of solutions for MIS problems with 100 to 170 qubits. The results suggest that there are QAOA parameters that generalize well across different COPs and annealing protocols, making TL a promising approach to reduce the need for classical optimization in finding optimal parameters for individual problems.This paper explores the application of transfer learning (TL) to optimize parameters for the Quantum Approximate Optimization Algorithm (QAOA) in solving combinatorial optimization problems (COPs). The authors investigate whether pre-optimized QAOA parameters from one problem instance can be effectively reused for different COP instances. They select six COPs: Traveling Salesman Problem (TSP), Bin Packing Problem (BPP), Knapsack Problem (KP), Weighted Maximum Cut (MaxCut), Maximal Independent Set (MIS), and Portfolio Optimization (PO). The study focuses on finding optimal $\beta$ and $\gamma$ parameters for $p$ layers of QAOA. The key findings are: 1. **BPP shows the best transferability**: Among all the COPs, BPP parameters transfer well to both larger instances of the same problem and other different problems. 2. **TL improves performance**: The probability of finding the optimal solution is above a quadratic speedup over random guessing for all problems, indicating good generalization capabilities. 3. **Real quantum hardware experiments**: The BPP parameters are tested on various quantum hardware devices, including Rigetti Aspen-M-3, IBM Brisbane, and IonQ Aria, for MIS problems with 8, 14, and 18 qubits. IonQ Aria shows the best overlap with the ideal probability distribution. 4. **Cross-platform TL**: The parameters from BPP are also transferred to a D-Wave Advantage quantum annealer, showing consistent improvement in the distribution of solutions for MIS problems with 100 to 170 qubits. The results suggest that there are QAOA parameters that generalize well across different COPs and annealing protocols, making TL a promising approach to reduce the need for classical optimization in finding optimal parameters for individual problems.