材料设计中炼金术优化的量子算法
Quantum algorithm for alchemical optimization in material design
摘要：
为特定应用开发定制材料是化学、材料科学和药物发现的一个活跃研究领域。可以从一组原子种类中获得的可能分子的数量随着系统的大小呈指数增长，限制了经典采样算法的效率。另一方面，量子计算机可以为化合物空间的采样提供有效的解决方案，以优化给定的分子性质。在这项工作中，我们提出了一个量子算法来解决材料设计问题，具有良好的缩放。这种方法的核心是将候选结构的空间表示为所有可能的原子组成的线性叠加。相应的“炼金术”哈密顿量驱动原子和电子空间的优化，导致选择最佳拟合分子，这优化了系统的给定性质，例如，药物设计中与外部潜力的相互作用。量子优势在于电子结构性质的有效计算以及指数大的化合物空间的采样。我们在模拟和IBM量子硬件中展示了我们方案的效率，并在一些测试案例中突出显示了结果。这些初步结果可以作为近期量子计算机的进一步材料设计量子算法的发展的基础。
英文摘要：
The development of tailored materials for specific applications is an active field of research in chemistry, material science and drug discovery. The number of possible molecules that can be obtained from a set of atomic species grow exponentially with the size of the system, limiting the efficiency of classical sampling algorithms. On the other hand, quantum computers can provide an efficient solution to the sampling of the chemical compound space for the optimization of a given molecular property. In this work we propose a quantum algorithm for addressing the material design problem with a favourable scaling. The core of this approach is the representation of the space of candidate structures as a linear superposition of all possible atomic compositions. The corresponding `alchemical' Hamiltonian drives then the optimization in both the atomic and electronic spaces leading to the selection of the best fitting molecule, which optimizes a given property of the system, e.g., the interaction with an external potential in drug design. The quantum advantage resides in the efficient calculation of the electronic structure properties together with the sampling of the exponentially large chemical compound space. We demonstrate both in simulations and in IBM Quantum hardware the efficiency of our scheme and highlight the results in a few test cases. These preliminary results can serve as a basis for the development of further material design quantum algorithms for near-term quantum computers.
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