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This paper presents QASPA-MaxCut, a shift-phase circuit approach for solving the Max-Cut problem on unweighted graphs using controlled rotations. Through exact state-vector simulations, it compares the performance of this method against standard QAOA restarts, highlighting the trade-offs between classical search budgets and shallow circuit depths. The study offers concrete insights into optimizing near-term quantum algorithms for combinatorial optimization tasks.
QASPA-MaxCut on Unweighted Graphs: Edge Encoding and QAOA Restarts
arXiv:2611.06698v1 Announce Type: new Abstract: Max-Cut is a common testbed for near-term quantum optimization because it has a simple Ising Hamiltonian, established classical baselines, and a parameterized quantum circuit whose performance depends on the outer classical optimizer. This paper develops QASPA-MaxCut, a shift-phase circuit for Max-Cut that encodes graph edges through controlled $R_y$ rotations of the form $\mathrm{CX}_{ij}R_y(\theta_{ij})\mathrm{CX}_{ij}$. We derive the exact two-qubit algebra of the edge block, trace an unweighted four-vertex example, and compare one-layer QASPA-MaxCut with depth-one QAOA by exact state-vector simulation. The protocol uses seed 42, evaluates connected backbone-augmented random graphs with additional-edge probabilities $p_{\mathrm{edge}}\in\{0.1,0.3,0.5,0.7\}$, and adds nineteen custom unweighted topologies. The QASPA-MaxCut score is reported after the algorithm's classical selection stage: the four leading QASPA bit strings are evaluated by the Max-Cut objective, and the best cut is retained. The QAOA score is the expected approximation ratio after local optimization, reported as a best-of-$k$ curve over one to ten restarts. In these small exact simulations, fixed-angle QASPA-MaxCut produces reported group-mean selection ratios between $0.955$ and $1.000$, the topology-level readout is unchanged in the retained finite-shot checks under the rule $\lceil 0.03047\cdot 2^n+1016.2\rceil$, and depth-one QAOA improves with additional classical restarts while remaining lower than the QASPA selection score in the reported mean summaries. This comparison motivates further study of the classical search budget needed to obtain competitive Max-Cut samples at shallow circuit depth.
Abstract:Max-Cut is a common testbed for near-term quantum optimization because it has a simple Ising Hamiltonian, established classical baselines, and a parameterized quantum circuit whose performance depends on the outer classical optimizer. This paper develops QASPA-MaxCut, a shift-phase circuit for Max-Cut that encodes graph edges through controlled $R_y$ rotations of the form $\mathrm{CX}_{ij}R_y(\theta_{ij})\mathrm{CX}_{ij}$. We derive the exact two-qubit algebra of the edge block, trace an unweighted four-vertex example, and compare one-layer QASPA-MaxCut with depth-one QAOA by exact state-vector simulation. The protocol uses seed 42, evaluates connected backbone-augmented random graphs with additional-edge probabilities $p_{\mathrm{edge}}\in\{0.1,0.3,0.5,0.7\}$, and adds nineteen custom unweighted topologies. The QASPA-MaxCut score is reported after the algorithm's classical selection stage: the four leading QASPA bit strings are evaluated by the Max-Cut objective, and the best cut is retained. The QAOA score is the expected approximation ratio after local optimization, reported as a best-of-$k$ curve over one to ten restarts. In these small exact simulations, fixed-angle QASPA-MaxCut produces reported group-mean selection ratios between $0.955$ and $1.000$, the topology-level readout is unchanged in the retained finite-shot checks under the rule $\lceil 0.03047\cdot 2^n+1016.2\rceil$, and depth-one QAOA improves with additional classical restarts while remaining lower than the QASPA selection score in the reported mean summaries. This comparison motivates further study of the classical search budget needed to obtain competitive Max-Cut samples at shallow circuit depth.
arXiv:2611.06698 [quant-ph]
(or arXiv:2611.06698v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2611.06698 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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