AI 中文总结
本文探讨量子计算在化学、材料科学等领域的科学应用，指出其核心挑战是获得化学意义的预测可靠性增益，近期价值来自工作流程整合，需结合特定场景判断实用性。
AI 中文摘要
分子与材料的预测模拟已产生广泛且重大的影响，但仍受限于准确处理电子关联、激发态及复杂能量景观的成本。量子计算提供了一种根本不同的计算范式，其中量子态被直接编码和操控，而非在经典硬件上近似处理。本文探讨了该方法在化学、材料科学及生物化学领域可能提供真正科学优势的方向，包括高精度处理关联活性空间、改进激发态模拟、加速组合结构空间探索。核心挑战并非仅量子比特扩展，而是需切实证明预测可靠性方面具有化学意义的增益。本文认为，近期价值最可能来自严谨的工作流程整合，而非完全替代经典方法。含噪声物理设备、经错误缓解的实用实验、早期容错设备及全容错量子计算机具有不同科学前景，实用性主张必须与所讨论的特定场景挂钩。当量子计算在计入态制备、测量、错误处理及与经典模拟耦合的全部成本后，能切实减少计算能量、速率、光谱或材料稳定性的不确定性时，它将具备科学价值。
英文摘要
The predictive simulation of molecules and materials has had a broad and significant impact. It nevertheless remains constrained by the cost of accurately treating electronic correlation, excited states, and complex energy landscapes. Quantum computing offers a fundamentally different computational paradigm in which quantum states are encoded and manipulated directly rather than approximated on classical hardware. Here we discuss where this approach may provide a genuine scientific advantage in chemistry, materials science, and biochemistry. Promising directions include the high-accuracy treatment of correlated active spaces, improved excited-state simulations, and accelerated exploration of combinatorial structure spaces. The central challenge is therefore not qubit scaling alone, but demonstrably chemically meaningful gains in predictive reliability. We argue that near-term value is most likely to come from disciplined workflow integration rather than wholesale replacement of classical methods. Noisy physical devices, error-mitigated utility experiments, early fault-tolerant devices, and fully fault-tolerant quantum computers offer different scientific prospects, and claims of usefulness must be tied to the specific regime being discussed. Quantum computing will become scientifically valuable when it demonstrably reduces uncertainty in computed energies, rates, spectra, or materials stability after the full costs of state preparation, measurement, error handling, and coupling to classical simulation are included.
