Quantum computing promises transformative advances, but remains constrained by recurring misconceptions and methodological pitfalls. This paper demonstrates a fundamental incompatibility between traditional agent-based modeling (ABM) implementations and quantum optimization frameworks like Quadratic Unconstrained Binary Optimization (QUBO). Using Schelling's segregation model as a case study, we show that the standard practice of directly translating ABM state observations into QUBO formulations not only fails to deliver quantum advantage, but actively undermines computational efficiency. The fundamental issue is architectural. Traditional ABM implementations entail observing the state of the system at each iteration, systematically destroying the quantum superposition required for computational advantage. Through analysis of Schelling's segregation dynamics on lollipop networks, we demonstrate how abandoning the QUBO reduction paradigm and instead reconceptualizing the research question, from "simulate agent dynamics iteratively until convergence" to "compute minimum of agent moves required for global satisfaction", enables a faster classical solution. This structural reconceptualization yields an algorithm that exploits network symmetries obscured in traditional ABM simulations and QUBO formulations. It establishes a new lower bound which quantum approaches must outperform to achieve advantage. Our work emphasizes that progress in quantum agent-based modeling does not require forcing classical ABM implementations into quantum frameworks. Instead, it should focus on clarifying when quantum advantage is structurally possible, developing best-in-class classical baselines through problem analysis, and fundamentally reformulating research questions rather than preserving classical iterative state change observation paradigms.
翻译:量子计算有望带来变革性进步,但仍受制于反复出现的误解和方法论陷阱。本文论证了传统基于代理的建模(ABM)实现与量子优化框架(如二次无约束二进制优化(QUBO))之间存在根本性不兼容。以谢林隔离模型为例,我们表明将ABM状态观测直接转化为QUBO公式的标准做法不仅无法实现量子优势,反而会损害计算效率。根本问题在于架构层面。传统ABM实现需要在每次迭代中观测系统状态,这系统地破坏了计算优势所需的量子叠加态。通过对棒棒糖网络上谢林隔离动态的分析,我们论证了如何放弃QUBO约简范式,并重新构建研究问题——从“迭代模拟代理动态直至收敛”转变为“计算实现全局满意度所需的最小代理移动次数”——从而获得更快的经典解决方案。这种结构性重构产生了一种算法,该算法利用了传统ABM模拟和QUBO公式中被掩盖的网络对称性。它建立了一个新的下界,量子方法必须超越该下界才能实现优势。我们的工作强调,量子基于代理建模的进展并不需要将经典ABM实现强行纳入量子框架,而应聚焦于厘清量子优势在结构上何时可能实现,通过问题分析开发最优经典基线,并从根本上重构研究问题,而非固守经典的迭代状态变化观测范式。