🤖 AI Summary
Quantum hardware noise significantly flattens the variational energy landscape of the Quantum Approximate Optimization Algorithm (QAOA) for constrained binary optimization problems, thereby degrading algorithmic performance. This work proposes a device-agnostic metric—Landscape Span Compression (LSC)—to quantify noise-induced landscape distortion. Experiments on IBM’s ibm_fez processor demonstrate that LSC robustly characterizes noise severity compared to four existing metrics and effectively informs parameter transfer and error mitigation strategies. The study reveals that noise compresses the landscape span by 24–30% without shifting the optimal solution; feasible solutions at the optimal parameters remain 1.5–1.7 times more probable than random sampling; calibrated noise models account for only ~42% of the observed performance degradation; and zero-noise extrapolation yields limited gains while substantially increasing uncertainty.
📝 Abstract
We introduce and empirically validate Landscape Span Compression (LSC), a device-agnostic metric for quantifying how hardware noise distorts the variational energy landscape of the Quantum Approximate Optimization Algorithm (QAOA). Intuitively, LSC measures how much noise flattens the energy landscape, approaching 1 as the landscape collapses toward a barren plateau. We report an experience study of applying QAOA with LSC-based noise characterization on IBM's ibm_fez for three constrained QUBO portfolio instances, distilling practical lessons for parameter transfer, calibration-model fidelity, and error mitigation. Running p=1 QAOA on ibm_fez (Heron r2, 156 qubits) with up to 57,344 shots per grid point across three constrained binary optimization instances encoded as QUBO problems, we find: (i) hardware noise uniformly compresses the landscape span by 24-30% without displacing the global minimum, supporting classical-to-hardware parameter transfer; (ii) feasibility fractions at the optimal parameters remain 1.5-1.7 times above random sampling despite noise-induced degradation; (iii) the IBM calibration-based noise model achieves Pearson r=0.959 structural agreement with hardware but explains only approximately 42% of approximation-ratio degradation, with crosstalk and coherent errors as the leading unexplained contributors; (iv) a consistent noise cost of approximately 0.03 approximation-ratio units is observed across all instances; and (v) Zero-Noise Extrapolation yields mixed energy improvements of +7%/+9%/-4% per instance with 3-5 times uncertainty inflation. We compare LSC against four existing metrics and argue it is the most robust discriminator of noise severity for constrained QAOA on near-term devices.