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This study retracts a previous device benchmark for the Quantum Approximate Optimization Algorithm (QAOA) on IBM Heron hardware, revealing that landscape compression is primarily driven by noise-induced feasibility loss rather than distinct circuit-fidelity effects. By scaling to 16 qubits, the authors demonstrate that energy landscape statistics are linearly correlated with the fraction of feasible samples, necessitating a revised protocol to separate true solution quality from hardware limitations. This correction provides critical clarity on interpreting QAOA performance metrics in noisy intermediate-scale quantum (NISQ) environments.
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Landscape Compression in Constrained QAOA Tracks Feasibility Loss on IBM Heron Hardware

arXiv2 min read

arXiv:2604.19426v2 Announce Type: replace Abstract: We study how hardware noise changes the p=1 Quantum Approximate Optimization Algorithm (QAOA) landscape for cardinality-constrained portfolio problems on ibm_fez (IBM Heron r2): seven instances from n=6 to 16 qubits, each on a 7x7 parameter grid at 4,096 and 8,192 shots per point. The first version of this paper proposed Landscape Span Compression (LSC), the fractional loss of energy range across the grid, as a device benchmark, based on stable values of 0.24-0.30 at n=0.9993) are uninformative and the only effect noise can remove is a raised feasible fraction. We withdraw the benchmark and noise-model-gap claims of the first version, correct its solution-quality figures, and describe a protocol that separates feasibility loss from solution quality.

Abstract:We study how hardware noise changes the p=1 Quantum Approximate Optimization Algorithm (QAOA) landscape for cardinality-constrained portfolio problems on ibm_fez (IBM Heron r2): seven instances from n=6 to 16 qubits, each on a 7x7 parameter grid at 4,096 and 8,192 shots per point. The first version of this paper proposed Landscape Span Compression (LSC), the fractional loss of energy range across the grid, as a device benchmark, based on stable values of 0.24-0.30 at n<=8. The larger study shows why that does not hold. Hardware LSC rises to 0.72-0.81 at n=16, growing with circuit size (R^2=0.96 and 0.88 for an exponential decay in CZ count). Because the landscape energy averages over all shots, it is nearly linear in the feasible fraction, and a statistic built only from that fraction reproduces LSC (Pearson r=0.954). For these penalty-encoded problems, landscape compression is a circuit-fidelity effect expressed as loss of feasible samples, as noise-flattening models of QAOA predict. The same mechanism biases the usual test of parameter transfer: grid verdicts on whether the optimum moves agree across shot counts for only 2 of 7 instances, and hardware optima move toward higher feasibility. Even in noiseless simulation, p=1 QAOA samples the feasible set of these instances almost uniformly, so best-feasible approximation ratios (>=0.9993) are uninformative and the only effect noise can remove is a raised feasible fraction. We withdraw the benchmark and noise-model-gap claims of the first version, correct its solution-quality figures, and describe a protocol that separates feasibility loss from solution quality.

7 pages, 4 figures, 7 tables. v2: substantially revised after a scaling study to n=16; withdraws the device-benchmark claim of v1 (changes listed in Sec. V). Data: doi:https://doi.org/10.5281/zenodo.23040298. Code: this https URL

Quantum Physics (quant-ph); Emerging Technologies (cs.ET)

arXiv:2604.19426 [quant-ph]

(or arXiv:2604.19426v2 [quant-ph] for this version)

Full text under the publisher's open licence · 295 words

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