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arXiv 2008.07553quant-ph

互信息辅助的自适应变分量子本征求解器

Mutual information-assisted Adaptive Variational Quantum Eigensolver

Zi-Jian Zhang, Thi Ha Kyaw, Jakob S. Kottmann, Matthias Degroote, Alán Aspuru-Guzik

AI总结:

该研究提出一种利用经典算法构建缩减规模纠缠门池的方法,通过经典近似基态量子比特间的互信息排序筛选纠缠门,结合密度矩阵重整化群预计算,在小分子实验中验证了小比例纠缠门池可达到原精度,为自适应变分量子算法拟设线路构建提供新路径。

摘要:

拟设线路的自适应构建为在近期量子硬件上实现可用的变分量子本征求解器提供了一条有前景的途径。这类算法旨在为特定问题构建最优线路,通过从预定义池中选择并添加纠缠门来自适应地构建拟设线路。在本研究中,我们提出了一种利用经典算法构建规模缩减的纠缠门池的方法。我们的方法使用经典近似基态中量子比特之间的互信息对纠缠门进行排序和筛选。本研究采用密度矩阵重整化群方法进行经典预计算。我们在小分子上通过数值实验验证了我们的方法。数值实验表明,仅包含原始纠缠门池一小部分的缩减纠缠门池可以达到相同的数值精度。我们认为该方法为变分量子算法的拟设线路自适应构建开辟了新途径。

英文摘要:

Adaptive construction of ansatz circuits offers a promising route towards applicable variational quantum eigensolvers on near-term quantum hardware. Those algorithms aim to build up optimal circuits for a certain problem and ansatz circuits are adaptively constructed by selecting and adding entanglers from a predefined pool. In this work, we propose a way to construct entangler pools with reduced size by leveraging classical algorithms. Our method uses mutual information between the qubits in classically approximated ground state to rank and screen the entanglers. The density matrix renormalization group method is employed for classical precomputation in this work. We corroborate our method numerically on small molecules. Our numerical experiments show that a reduced entangler pool with a small portion of the original entangler pool can achieve same numerical accuracy. We believe that our method paves a new way for adaptive construction of ansatz circuits for variational quantum algorithms.

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