Curator's Take
This research tackles one of the most pressing challenges in near-term quantum computing by developing a practical framework to understand how hardware noise systematically degrades QAOA performance on real quantum devices. The introduction of Landscape Span Compression as a device-agnostic metric provides researchers with a concrete tool to quantify noise-induced landscape flattening, while the extensive empirical validation on IBM's 156-qubit Heron processor offers valuable insights for optimizing variational quantum algorithms in noisy environments. Particularly significant is the finding that hardware noise compresses energy landscapes by 24-30% without shifting optimal parameters, suggesting that classical optimization results can still inform quantum hardware deployment strategies. The work also reveals important limitations of current calibration models, which capture structural behavior well but miss nearly 60% of performance degradation, highlighting the need for better noise characterization methods as quantum devices scale up.
— Mark Eatherly
Summary
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.