Julia versus Python for Quantum Computing: A Comparative Study of Performance, Scalability, and Scientific
Sayed Mohammad Badiezadegan
Quantum computing research relies heavily on numerical simulation, linear algebra, optimization, and scientific programming. While Python has become the dominant language for quantum computing frameworks such as Qiskit, Cirq, and PennyLane, Julia has emerged as a promising alternative due to its high-performance execution model and modern language design. This paper investigates the advantages of Julia over Python for quantum computing applications. We compare the two languages from theoretical, architectural, and practical perspectives, focusing on execution speed, memory efficiency, parallelization, numerical accuracy, and suitability for large-scale quantum simulation. Furthermore, we present an experimental study based on the Quantum Approximate Optimization Algorithm (QAOA) applied to the MaxCut problem. The benchmark was implemented independently in both Qiskit/Python and Julia and executed on the same hardware platform. Preliminary experimental results demonstrate a significant reduction in execution time when using Julia, with an observed speedup of approximately 3.9 x. Source code for both implementations has been made publicly available through the author's GitHub repository to facilitate reproducibility and further research.
•Jun 12, 2026•0 citations