Migrating QAOA from Qiskit 1.x to 2.x: An experience report

• URL: http://arxiv.org/abs/2512.08245v1
• Date: Tue, 09 Dec 2025 04:58:56 GMT
• Title: Migrating QAOA from Qiskit 1.x to 2.x: An experience report
• Abstract summary: Migrating quantum algorithms across evolving frameworks introduces subtle behavioral changes that affect accuracy and algorithms.<n>This paper reports our experience converting the Quantum Algorithm Optimization (QAOA) from Qiskit with Qiskit 1.x (v1 primitives) to a custom implementation using Qiskit 2.x (v2 primitives)<n>Despite identical circuits, Approximates, and Hamiltonians, the new version produced drastically different results.
• Score: 0.3679863727963206
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Migrating quantum algorithms across evolving frameworks introduces subtle behavioral changes that affect accuracy and reproducibility. This paper reports our experience converting the Quantum Approximate Optimization Algorithm (QAOA) from Qiskit Algorithms with Qiskit 1.x (v1 primitives) to a custom implementation using Qiskit 2.x (v2 primitives). Despite identical circuits, optimizers, and Hamiltonians, the new version produced drastically different results. A systematic analysis revealed the root cause: the sampling budget -- the number of circuit executions (shots) per iteration. The library's implicit use of unlimited shots yielded dense probability distributions, whereas the v2 default of 10 000 shots captured only 23% of the state space. Increasing shots to 250 000 restored library-level accuracy. This study highlights how hidden parameters at the quantum--classical interaction level can dominate hybrid algorithm performance and provides actionable recommendations for developers and framework designers to ensure reproducible results in quantum software migration.

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