Optimizing of a portfolio of financial assets is a critical industrial problem which can be approximately solved using algorithms suitable for quantum processing units (QPUs). We benchmark the success of this approach using the Quantum Approximate Optimization Algorithm (QAOA); an algorithm targeting gate-model QPUs. Our focus is on the quality of solutions achieved as determined by the Normalized and Complementary Wasserstein Distance,
η, which we present in a manner to expose the QAOA as a transporter of probability. Using
η as an application specific benchmark of performance, we measure it on selection of QPUs as a function of QAOA circuit depth
p. At
n=2 (2 qubits) we find peak solution quality at
p=5 for most systems and for
n=3 this peak is at
p=4 on a trapped ion QPU. Increasing solution quality with
p is also observed using variants of the more general Quantum Alternating Operator Ansätz at
p=2 for
n=2 and
3 which has not been previously reported. In identical measurements,
η is observed to be variable at a level exceeding the noise produced from the finite number of shots. This suggests that variability itself should be regarded as a QPU performance benchmark for given applications. While studying the ideal execution of QAOA, we find that
p=1 solution quality degrades when the portfolio budget
B approaches
n/2 and increases when
B≈1 or
n−1. This trend directly corresponds to the binomial coefficient
nCB and is connected with the recently reported phenomenon of reachability deficits. Derivative-requiring and derivative-free classical optimizers are benchmarked on the basis of the achieved
η beyond
p=1 to find that derivative-free optimizers are generally more effective for the given computational resources, problem sizes and circuit depths.
In this paper, we present QPack, a universal benchmark for Noisy Intermediate-Scale Quantum (NISQ) computers based on Quantum Approximate Optimization Algorithms (QAOA). Unlike other evaluation metrics in the field, this benchmark evaluates not only one, but multiple important aspects of quantum computing hardware: the maximum problem size a quantum computer can solve, the required runtime, as well as the achieved accuracy. The applications MaxCut, dominating set and traveling salesman are included to provide variation in resource requirements. This will allow for a diverse benchmark that promotes optimal design considerations, avoiding hardware implementations for specific applications. We also discuss the design aspects that are taken in consideration for the QPack benchmark, with critical quantum benchmark requirements in mind. An implementation is presented, providing practical metrics. QPack is presented as a hardware agnostic benchmark by making use of the XACC library. We demonstrate the application of the benchmark on various IBM machines, as well as a range of simulators.
Exploratory Benchmark — Applied Quantum Computing Track This record presents an exploratory optimization benchmark in the applied quantum computing track of the Radial Coherential Dynamics (RCD) research program. Building upon the previously published decoherence suppression model (Topological Decoherence Suppression via Coherential Geometry, Zenodo, December 2025, DOI: 10.5281/zenodo.18000641) and its prior applications to variational quantum eigensolvers on molecular and lattice systems, this work explores the transfer of a fixed decoherence regulation law to a quantum optimization algorithm. The Quantum Approximate Optimization Algorithm (QAOA) is applied to the MaxCut problem on a fixed graph. Performance is evaluated using the approximation ratio and the gap to the optimal solution as primary metrics. A controlled A/B methodology is employed: identical QAOA circuits are executed using standard noise parameters (control) and RCD-regulated noise parameters (treatment), where the dephasing time is rescaled according to the previously published lawT₂ → T₂ / √α, with fixed α ≈ 1.3 × 10⁻⁴. No additional parameters or algorithmic modifications are introduced. The results show a consistent improvement in optimization performance, with an average increase in approximation ratio of approximately 9–10% and a reduction of the optimization gap of approximately 25%, with the effect becoming more pronounced at higher circuit depths. This benchmark does not claim algorithmic optimality or quantum advantage. Its purpose is to assess whether a decoherence suppression law—originally derived for single-qubit dynamics—produces consistent and transferable effects when applied, without tuning, to variational quantum optimization algorithms. All code, data, and figures are provided for independent evaluation by the scientific community.