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This development demonstrates how specialized machine learning frameworks like MLX can significantly accelerate quantum circuit simulations on consumer-grade Apple Silicon hardware. By leveraging optimized kernels and Metal shaders, researchers can now perform complex non-equilibrium studies, such as analyzing measurement-induced phase transitions, without relying on large-scale clusters. This lowers the barrier to entry for quantum algorithm development and error correction research by making powerful simulation tools more accessible.
Apple Silicon Simulators Hit 33x Speedup with MLX Kernels
A new simulator, Qupertino, uses MLX array operations for quantum circuit simulation on Apple Silicon. The "pure tier" uses specialized MLX kernels, achieving a 25-33x speedup over baseline implementations for operations like diagonal and controlled gates.
Good day, quantum explorers. A new simulator, Qupertino, uses MLX array operations for quantum circuit simulation on Apple Silicon. The "pure tier" uses specialized MLX kernels, achieving a 25-33x speedup over baseline implementations for operations like diagonal and controlled gates. An optional "shader tier" adds hand-written Metal kernels for complex gate families, using runtime fusion detectors for accuracy. Qupertino offers…
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Qupertino: Pure MLX Array Kernels versus Hand-Tuned Metal Shaders for Quantum Circuit Simulation on Apple Silicon
arXiv · 18 sept. 2026
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