The paper introduces *Noise-Directed Adaptive Remapping* (NDAR), a heuristic algorithm designed to improve the performance of quantum approximate optimization (QAOA) by leveraging certain types of noise. NDAR bootstraps the noise attractor state by iteratively gauge-transforming the cost-function Hamiltonian, transforming the noise attractor into higher-quality solutions. The algorithm is implemented on Rigetti's quantum device, achieving approximation ratios of 0.9-0.96 for random, fully connected graphs on 82 qubits using only depth 1 QAOA with NDAR, compared to 0.34-0.51 for standard QAOA with the same number of function calls. The authors demonstrate that NDAR effectively aligns the attractor state with high-quality solutions, improving the overall optimization performance. The paper also discusses the theoretical foundations of NDAR, its experimental validation, and potential future directions for further research.The paper introduces *Noise-Directed Adaptive Remapping* (NDAR), a heuristic algorithm designed to improve the performance of quantum approximate optimization (QAOA) by leveraging certain types of noise. NDAR bootstraps the noise attractor state by iteratively gauge-transforming the cost-function Hamiltonian, transforming the noise attractor into higher-quality solutions. The algorithm is implemented on Rigetti's quantum device, achieving approximation ratios of 0.9-0.96 for random, fully connected graphs on 82 qubits using only depth 1 QAOA with NDAR, compared to 0.34-0.51 for standard QAOA with the same number of function calls. The authors demonstrate that NDAR effectively aligns the attractor state with high-quality solutions, improving the overall optimization performance. The paper also discusses the theoretical foundations of NDAR, its experimental validation, and potential future directions for further research.