心智观察所|量子计算的瓶颈：处理器再快，也必须等待数据

Core Viewpoint - The article argues that the current discourse on quantum computing often overlooks the fundamental truth that computational power is not solely determined by processor speed, but rather by data storage, scheduling, and access efficiency [1][3][6]. Group 1: Understanding Computational Power - Computational power is frequently misunderstood as equivalent to CPU speed, but modern computing performance bottlenecks arise from data storage and access rather than the CPU itself [3][5]. - The evolution of computational demands is driven by the increasing scale of data rather than the speed of calculations, particularly in fields like machine learning where managing large datasets is crucial [5][6]. Group 2: Challenges of Quantum Computing - Quantum computing is heavily reliant on classical memory, facing significant structural constraints in data storage and scheduling, which complicates its potential for sustainable computational power growth [7][8]. - Quantum bits (qubits) are not suitable for long-term data storage, as they are inherently unstable and cannot maintain quantum states over extended periods, making them less effective than classical memory [7][8]. - The theoretical and engineering challenges of quantum storage mean that it cannot fulfill the roles of classical memory, leading to limitations in scalability and efficiency [8][9]. Group 3: Implications for Future Development - Even with advancements in quantum processing speed, the overall computational power of quantum computers is unlikely to exceed that of classical computers due to the persistent reliance on classical memory for data organization and support [9][10]. - The structural contradictions in quantum computing highlight the need for a realistic assessment of its capabilities, especially in data-driven contexts where memory capacity and bandwidth are critical [10].