论文标题
荟萃变量量子本质索（meta-vqe）：用于量子模拟的参数化汉密尔顿的学习能量曲线
The Meta-Variational Quantum Eigensolver (Meta-VQE): Learning energy profiles of parameterized Hamiltonians for quantum simulation
论文作者
论文摘要
我们提出了一种能够学习参数化哈密顿量的基态能量谱的算法。通过训练具有几个数据点的元VQE，它提供了一个初始电路参数化，可用于计算哈密顿在某个信任区域内的任何参数化的基态能量。我们使用XXZ自旋链，电子H $ _ {4} $ hamiltonian和单跨型量子模拟测试该算法。在所有情况下，元VQE都能够学习能量功能的形状，在某些情况下，与单个VQE优化相比，精度提高了精度。 Meta-VQE算法从优化的数量方面引入了参数化的汉密尔顿人的效率，也引入了单个优化的量子电路参数的良好起点。提出的算法建议可以与变化算法领域的其他改进相结合，以缩短当前最新技术和具有量子优势的应用之间的距离。
We present the meta-VQE, an algorithm capable to learn the ground state energy profile of a parametrized Hamiltonian. By training the meta-VQE with a few data points, it delivers an initial circuit parametrization that can be used to compute the ground state energy of any parametrization of the Hamiltonian within a certain trust region. We test this algorithm with a XXZ spin chain, an electronic H$_{4}$ Hamiltonian and a single-transmon quantum simulation. In all cases, the meta-VQE is able to learn the shape of the energy functional and, in some cases, resulted in improved accuracy in comparison to individual VQE optimization. The meta-VQE algorithm introduces both a gain in efficiency for parametrized Hamiltonians, in terms of the number of optimizations, and a good starting point for the quantum circuit parameters for individual optimizations. The proposed algorithm proposal can be readily mixed with other improvements in the field of variational algorithms to shorten the distance between the current state-of-the-art and applications with quantum advantage.
