The paper introduces Noise-Directed Adaptive Remapping (NDAR), a new algorithmic framework for quantum approximate optimization in the presence of certain types of noise. NDAR iteratively changes the encoding of the target Hamiltonian in a way that adaptively attempts to align the quantum evolution with the noise dynamics. The algorithm leverages the knowledge of noise to guide the optimization process, transforming the noise attractor state into higher-quality solutions. This approach improves the performance of the Quantum Approximate Optimization Algorithm (QAOA) by using noise to aid variational optimization rather than hindering it. Experiments on Rigetti's 82-qubit quantum processor show that NDAR significantly improves the approximation ratios of QAOA, achieving ratios of 0.9-0.96 for random, fully connected graphs, compared to 0.34-0.51 for standard QAOA. The results demonstrate that NDAR can effectively enhance the performance of quantum optimization algorithms by adaptively remapping the problem based on the noise characteristics. The algorithm is applicable to a wide variety of quantum and classical algorithms, especially those categorized as Ising machines. The study highlights the potential of NDAR as a standard method in quantum optimization, particularly for devices affected by amplitude-damping-like noise.The paper introduces Noise-Directed Adaptive Remapping (NDAR), a new algorithmic framework for quantum approximate optimization in the presence of certain types of noise. NDAR iteratively changes the encoding of the target Hamiltonian in a way that adaptively attempts to align the quantum evolution with the noise dynamics. The algorithm leverages the knowledge of noise to guide the optimization process, transforming the noise attractor state into higher-quality solutions. This approach improves the performance of the Quantum Approximate Optimization Algorithm (QAOA) by using noise to aid variational optimization rather than hindering it. Experiments on Rigetti's 82-qubit quantum processor show that NDAR significantly improves the approximation ratios of QAOA, achieving ratios of 0.9-0.96 for random, fully connected graphs, compared to 0.34-0.51 for standard QAOA. The results demonstrate that NDAR can effectively enhance the performance of quantum optimization algorithms by adaptively remapping the problem based on the noise characteristics. The algorithm is applicable to a wide variety of quantum and classical algorithms, especially those categorized as Ising machines. The study highlights the potential of NDAR as a standard method in quantum optimization, particularly for devices affected by amplitude-damping-like noise.