Traore Diata, Adjoua Olivier, Feniou César, Lygatsika Ioanna-Maria, Maday Yvon, Posenitskiy Evgeny, Hammernik Kerstin, Peruzzo Alberto, Toulouse Julien, Giner Emmanuel, Piquemal Jean-Philip
Sorbonne Université, LCT, UMR 7616 CNRS, 75005, Paris, France.
Qubit Pharmaceuticals, Advanced Research Department, 75014, Paris, France.
Commun Chem. 2024 Nov 18;7(1):269. doi: 10.1038/s42004-024-01348-3.
Using GPU-accelerated state-vector emulation, we propose to embed a quantum computing ansatz into density-functional theory via density-based basis-set corrections to obtain quantitative quantum-chemistry results on molecules that would otherwise require brute-force quantum calculations using hundreds of logical qubits. Indeed, accessing a quantitative description of chemical systems while minimizing quantum resources is an essential challenge given the limited qubit capabilities of current quantum processors. We provide a shortcut towards chemically accurate quantum computations by approaching the complete-basis-set limit through coupling the density-based basis-set corrections approach, applied to any given variational ansatz, to an on-the-fly crafting of basis sets specifically adapted to a given system and user-defined qubit budget. The resulting approach self-consistently accelerates the basis-set convergence, improving electronic densities, ground-state energies, and first-order properties (e.g. dipole moments), but can also serve as a classical, a posteriori, energy correction to quantum hardware calculations with expected applications in drug design and materials science.
利用GPU加速的态矢量仿真，我们建议通过基于密度的基组校正将量子计算假设嵌入密度泛函理论，以获得分子的定量量子化学结果，否则这些分子需要使用数百个逻辑量子比特进行蛮力量子计算。事实上，鉴于当前量子处理器的量子比特能力有限，在最小化量子资源的同时获得化学系统的定量描述是一项重大挑战。我们通过将应用于任何给定变分假设的基于密度的基组校正方法与专门针对给定系统和用户定义的量子比特预算的基组实时构建相结合，朝着化学精确的量子计算提供了一条捷径。由此产生的方法自洽地加速了基组收敛，改善了电子密度、基态能量和一阶性质（如偶极矩），但也可以作为对量子硬件计算的经典后验能量校正，有望应用于药物设计和材料科学。
