aiqbench analysis
This study provides critical hardware characterization data for quantum circuit simulation on Apple M4 Pro Unified Memory Architecture, revealing that performance is strictly memory-bandwidth bound rather than compute-limited. The research identifies a significant timing discontinuity at the 28-to-29 qubit transition and demonstrates that irregular memory access patterns can yield speedups far exceeding standard STREAM predictions. These findings offer an essential framework for optimizing simulation backends on UMA systems where CPU and GPU share identical DRAM pools.
A Controlled Study of Memory Hierarchy Transitions in Quantum Circuit Simulation on Apple M4 Pro Unified Memory Architecture
arXiv:2605.08792v3 Announce Type: replace-cross Abstract: State-vector quantum circuit simulation is memory-bandwidth bound, yet the interaction between memory hierarchy, access pattern, and hardware parallelism remains incompletely characterized. We address this using the Apple M4 Pro Unified Memory Architecture (UMA), where CPU and GPU share identical physical LPDDR5X DRAM ($\sim$224 GB/s STREAM bandwidth for both), eliminating memory-technology and interconnect confounds. Using a thermally isolated, multi-trial methodology across 11 simulation backends on GHZ and QFT circuits from 3 to 30 qubits, we make three central contributions. First, a Roofline analysis confirms all gate implementations have arithmetic intensity $\leq$0.38 FLOP/byte, well below the ridge point for any plausible peak compute on modern hardware, establishing structural memory-boundedness. Second, we identify a reproducible 4.46$\times$ timing discontinuity at the 28$\rightarrow$29 qubit transition, confirmed under thermally isolated conditions and cross-validated across GHZ and QFT circuits; tensordot backends exhibit the full discontinuity while direct-index backends maintain $\sim$2$\times$ per-qubit scaling throughout. Third, despite STREAM predicting only 1.85$\times$ GPU speedup (MLX CPU 119.9 GB/s vs. MLX GPU 221.9 GB/s), all three algorithm classes exceed this prediction: tensordot 3.1--4.1$\times$, flat-index 3.5--5.9$\times$, and direct-index 6--10$\times$, demonstrating that peak streaming bandwidth does not predict simulation speedup for non-contiguous memory access patterns, with the gap widening as access irregularity increases. These findings provide a hardware-characterization framework for quantum simulation workloads on UMA.
Abstract:State-vector quantum circuit simulation is memory-bandwidth bound, yet the interaction between memory hierarchy, access pattern, and hardware parallelism remains incompletely characterized. We address this using the Apple M4 Pro Unified Memory Architecture (UMA), where CPU and GPU share identical physical LPDDR5X DRAM ($\sim$224 GB/s STREAM bandwidth for both), eliminating memory-technology and interconnect confounds. Using a thermally isolated, multi-trial methodology across 11 simulation backends on GHZ and QFT circuits from 3 to 30 qubits, we make three central contributions. First, a Roofline analysis confirms all gate implementations have arithmetic intensity $\leq$0.38 FLOP/byte, well below the ridge point for any plausible peak compute on modern hardware, establishing structural memory-boundedness. Second, we identify a reproducible 4.46$\times$ timing discontinuity at the 28$\rightarrow$29 qubit transition, confirmed under thermally isolated conditions and cross-validated across GHZ and QFT circuits; tensordot backends exhibit the full discontinuity while direct-index backends maintain $\sim$2$\times$ per-qubit scaling throughout. Third, despite STREAM predicting only 1.85$\times$ GPU speedup (MLX CPU 119.9 GB/s vs. MLX GPU 221.9 GB/s), all three algorithm classes exceed this prediction: tensordot 3.1--4.1$\times$, flat-index 3.5--5.9$\times$, and direct-index 6--10$\times$, demonstrating that peak streaming bandwidth does not predict simulation speedup for non-contiguous memory access patterns, with the gap widening as access irregularity increases. These findings provide a hardware-characterization framework for quantum simulation workloads on UMA.
Performance (cs.PF); Quantum Physics (quant-ph)
arXiv:2605.08792 [cs.PF]
(or arXiv:2605.08792v3 [cs.PF] for this version)
https://doi.org/10.48550/arXiv.2605.08792 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 306 words
Related coverage

News
Quantum Computing Report · Jul 25, 2026

News
Quantum Computing Report · Jun 7, 2026
News Archive 2026
Quantum Computing Report · Jul 25, 2026

IonQ Researchers Run MegaQuOp-Scale Quantum Error Decoder on a MacBook Pro
The Quantum Insider · Aug 31, 2026