Comparative Analysis of Quantum and Classical Computing: Performance, Error Rates, and Hybrid Architectures - Silva Atencio, Gabriel (EAI Endorsed Transactions on Internet of Things, 2026)
Classical computing is approaching its physical limits, so quantum computing as an alternative model requires careful evaluation. This study compares various aspects of two systems by looking at formal algorithmic complexity, simulation-based comparisons with realistic noise models, quantitative meta-analysis, figuring out the difference between physical and logical error rates, and clearly defining contributions. A thorough study examined 24,100 papers published between 2015 and 2024 from leading scientific databases. A study of algorithmic complexity using Big-O notation confirmed that Shor’s algorithm is faster than standard O(exp((64/9)²/³ (log N)²/³ (log log N)²/³)) by a super polynomial factor, Grover’s algorithm is faster by a quadratic factor (O(√N) versus O(N)), and quantum simulation is faster by an exponential factor. There were Five quantum machines exhibited physical error rates ranging from 10⁻² to 10⁻⁵. Simulation-based testing revealed that the quantum advantage emerges above certain complexity thresholds: at extremely high complexity, quantum processing reached 10.3 μs versus 40.2 μs for classical computing, with better error resistance (2.3% for quantum vs. 5.9% for classical). Quantum computing has real benefits in terms of speed, mistake tolerance, and energy economy for problems that are too complicated to solve with traditional methods. However, significant challenges remain in scalability and workforce development. Hybrid quantum-classical models offer the most promising path to near-term practical benefits. On the other hand, the shift to postquantum security needs instant attention, regardless of when quantum hardware will be available.