超越硬件：通过状态代理均衡实现自适应算法控制
Beyond Hardware: Adaptive Algorithmic Control by State-Proxy Equalization
- National University of Singapore（新加坡国立大学）
- Zuse Institute Berlin（柏林祖斯研究所）
- Technische Universität Berlin（柏林工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出自适应算法控制（A2C）软件范式，基于状态代理均衡定理优化计算资源分配，在多达156量子比特的优化问题中将低能采样概率提升22%至超100,000%，证明软件途径可显著补充硬件改进。
AI中文摘要：
量子计算的最新进展主要由硬件的改进所驱动。在此，我们表明，显著的收益也可以源于在整个量子计算过程中如何分配有限的计算资源。我们引入了自适应算法控制（A2C），这是一种基于状态代理均衡定理的软件范式，该定理证明了对于状态导出的代理误差泛函，最优分配均衡的是累积计算难度而非物理时间。所需的计算难度直接从演化的量子态中推断，避免了对指数级庞大的多体谱的显式重构。在包含多达156个量子比特的量子优化问题中，结合精确模拟、大规模超级计算机计算和IBM量子硬件实验，A2C在匹配的电路深度和测量预算下，将低能采样概率提高了22%至超过100,000%。这些结果表明，量子计算性能不仅取决于硬件能力，还取决于有限计算资源的组织方式，确立了自适应算法控制作为推进量子计算的补充软件途径。
英文摘要：
Recent advances in quantum computing have been driven primarily by improvements in hardware. Here we show that substantial gains can instead arise from how finite computational resources are allocated throughout a quantum computation. We introduce Adaptive Algorithmic Control (A2C), a software paradigm founded on a State-Proxy Equalization theorem, which proves that the optimal allocation for a state-derived proxy-error functional equalizes cumulative computational hardness rather than physical time. The required computational hardness is inferred directly from the evolving quantum state, avoiding explicit reconstruction of the exponentially large many-body spectrum. Across quantum optimization problems containing up to 156 qubits, combining exact simulations, large-scale supercomputer computations and IBM quantum hardware experiments, A2C improves the low-energy sampling probabilities by $22\%$ to over $100,000\%$ under matched circuit depths and measurement budgets. These results demonstrate that quantum computational performance depends not only on hardware capabilities, but also on how finite computational resources are organized, establishing adaptive algorithmic control as a complementary software pathway for advancing quantum computation.
