论文标题
使用特征向量延续在量子计算机上的子空间对角线化
Subspace Diagonalization on Quantum Computers using Eigenvector Continuation
论文作者
论文摘要
量子子空间对角线化（QSD）方法是量子古典杂种方法，通常用于通过将哈密顿量投射到较小的子空间来找到地面和激发态能量。在应用这些方面，从基础完整性和量子计算机实施效率的角度来看，子空间基础的选择至关重要。在这项工作中，我们将特征向量延续（EC）作为QSD方法，其中选择了参数空间中不同点的汉密尔顿的低能状态作为子空间基础。这种独特的选择可以通过最少的硬件工作来快速评估低能光谱，包括地面和附近的兴奋状态。作为一个特别的优势，EC能够捕获与问题的不同对称扇区相对应的基态跨界跨态频谱。我们演示了这种相互作用的自旋模型和分子的方法。
Quantum subspace diagonalization (QSD) methods are quantum-classical hybrid methods, commonly used to find ground and excited state energies by projecting the Hamiltonian to a smaller subspace. In applying these, the choice of subspace basis is critical from the perspectives of basis completeness and efficiency of implementation on quantum computers. In this work, we present Eigenvector Continuation (EC) as a QSD method, where low-energy states of the Hamiltonian at different points in parameter space are chosen as the subspace basis. This unique choice enables rapid evaluation of low-energy spectra, including ground and nearby excited states, with minimal hardware effort. As a particular advantage, EC is able to capture the spectrum across ground state crossovers corresponding to different symmetry sectors of the problem. We demonstrate this method for interacting spin models and molecules.
