Optimizing Cost Hamiltonian Compilation for Max-Cut QAOA on Unweighted Graphs Using Global Controls and Qubit Bit Flips

• URL: http://arxiv.org/abs/2509.00170v1
• Date: Fri, 29 Aug 2025 18:12:20 GMT
• Title: Optimizing Cost Hamiltonian Compilation for Max-Cut QAOA on Unweighted Graphs Using Global Controls and Qubit Bit Flips
• Authors: Saber Dinpazhouh, Illya V. Hicks,
• Abstract summary: We study a cost Hamiltonian compilation problem for the quantum approximate optimization algorithm (QAOA) applied to the Max-Cut problem.<n>Instead of standard compilation with CNOT and Rz gates, we employ global coupling operations and single-qubit bit flips.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study a cost Hamiltonian compilation problem for the quantum approximate optimization algorithm (QAOA) applied to the Max-Cut problem, focusing on trapped-ion quantum computers. Instead of standard compilation with CNOT and Rz gates, we employ global coupling operations and single-qubit bit flips. Prior work by Rajakumar et al. established that such a compilation is always possible. Minimizing operational error requires short operation sequences. The problem reduces to a low-rank semi-discrete decomposition of the graph's adjacency matrix, where the minimum achievable rank, the graph coupling number gc(G), represents the number of global control layers. Rajakumar et al. introduced the Union of Stars construction, proving gc(G) <= 3n - 2 for unweighted graphs with n vertices, and gave an O(m)-rank construction for weighted graphs. We concentrate on unweighted graphs. We derive structural properties of the compilation problem and show the Union of Stars method is order-optimal by proving a lower bound of gc(G) >= n - 1 for a family of graphs. We also improve the general upper bound to 2.5n + 2. For particular graph families -- cliques, perfect matchings, paths, and cycles -- we provide sharper bounds. Further, we reveal a link between the problem and Hadamard matrix theory. Finally, we introduce a compact mixed-integer programming (MIP) formulation that outperforms the previously studied exponential-size MIP.

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