从愿景到实践：缩小量子计算的应用差距
From Promise to Practice: Closing the Application Gap in Quantum Computing
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中文总结 AI 辅助
本文指出量子计算需兼顾自下而上的技术发展与自上而下的工业需求，通过构建明确接口与中间层缩小应用差距，其机遇在于深度物理建模与高价值决策的协同设计。
中文摘要 AI 辅助
量子计算是一项深度技术，仅靠单一方向无法有效推动其发展。尽管该领域已涌现出大量基于数学原理的算法加速成果，但产业影响同样依赖于从真实的工业决策场景出发，向下推导需计算、验证和集成的内容。在本视角文章中，笔者认为持续的进步需要同时兼顾两个方向：一是从物理、硬件和算法出发的自下而上发展，二是从工业需求与约束出发的自上而下发展，二者同等重要且需持续耦合。这种双视角并非为了平衡而平衡：量子计算机无法解决任意问题，因此与产业的互动必须锚定算法可处理性；然而，可处理的计算若不与成熟工作流程中的决策点（如药物发现中的候选分子筛选、具有改进空气动力学特性的新型飞机外形设计等）关联，便很难产生价值。笔者分析了历史叙事与专业领域的结构性分离如何延缓了这种耦合的形成，并概述了构建该耦合所需的条件：技术团队与领域场景之间的明确接口，以及能将量子计算输出转化为与决策相关的可观测结果、同时又不抑制基础创新的中间层。以此视角来看，量子计算的机遇在深度物理建模与高价值决策交汇之处最为清晰，前提是该领域从一开始就对双方进行协同设计。
英文摘要
Quantum computing is a deep technology whose progress cannot be driven effectively from one direction alone. While the field has developed a growing catalogue of mathematically grounded algorithmic speedups, industrial impact will depend just as much on starting from real industrial decision contexts and working downward to what must be computed, validated and integrated. In this Perspective, I argue that sustained progress requires treating these two directions: bottom-up development from physics, hardware and algorithms, and top-down development from industrial needs and constraints. Equally primary and continuously coupled. This dual-viewpoint is not a matter of balance for its own sake. Quantum computers cannot solve arbitrary problems, so engagement with industry must remain anchored in algorithmic tractability. Yet tractable computations are rarely valuable unless they connect to decision points in established workflows such as candidate selection in drug discovery or the design of a new aircraft shape with improved aerodynamics. I analyse how historical narratives and structural separations of expertise slowed the formation of this coupling and outline what it takes to build it: explicit interfaces between technical teams and domain context and intermediate layers that translate quantum outputs into decision-relevant observables without suffocating foundational innovation. Framed this way, quantum computing's opportunity is clearest where deep physical modelling meets high-value decisions. Provided the field co-designs both sides from the outset.
