A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolver
A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolver
复制标题
DOI：
--
复制
发表时间：
2019-10
期刊：
影响因子：
--
通讯作者：
Arthur G. Rattew;Shaohan Hu;Marco Pistoia;Richard Chen;Stephen P Wood
中科院分区：
文献类型：
--
作者：
Arthur G. Rattew;Shaohan Hu;Marco Pistoia;Richard Chen;Stephen P Wood
由于变分量子算法在解决科学和商业问题方面的多功能性，它在许多领域都显示出了光明的前景。然而，用于哈密顿模拟的主要算法，如变分量子本征解算器(VQE)，使用固定的预先构造的解析器，限制了它们的普遍适用性和准确性。因此，变分形式-实现反变换的量子电路-要么是启发式地制作，要么是通过编码特定领域的知识来制作。本文提出了一种进化变分量子特征解算器(EVQE)，它是一种新的变分算法，它利用进化规划技术，通过动态生成和优化一个ansatz来最小化给定的哈密顿量的期望值。该算法同样适用于所有领域的优化问题，以硬件高效的方式获得准确的能量评估。在分子模拟中，EVQE产生的变分形式比具有么正耦合团簇的VQE产生的变分形式低18.6倍，所用CX门少12倍。EVQE表现出显著的抗噪声性能，在噪声模拟中获得的结果比使用任何测试的ANSATZ配置的VQE误差至少少3.6倍。我们在一台真实的5量子位IBMQ量子计算机上成功地评估了EVQE。我们通过模拟和在真实量子硬件上获得的实验结果表明，EVQE在目前和不久的将来对量子计算机的通用优化是有效的。
Variational quantum algorithms have shown promise in numerous fields due to their versatility in solving problems of scientific and commercial interest. However, leading algorithms for Hamiltonian simulation, such as the Variational Quantum Eigensolver (VQE), use fixed preconstructed ansatzes, limiting their general applicability and accuracy. Thus, variational forms---the quantum circuits that implement ansatzes ---are either crafted heuristically or by encoding domain-specific knowledge. In this paper, we present an Evolutionary Variational Quantum Eigensolver (EVQE), a novel variational algorithm that uses evolutionary programming techniques to minimize the expectation value of a given Hamiltonian by dynamically generating and optimizing an ansatz. The algorithm is equally applicable to optimization problems in all domains, obtaining accurate energy evaluations with hardware-efficient ansatzes. In molecular simulations, the variational forms generated by EVQE are up to $18.6\times$ shallower and use up to $12\times$ fewer CX gates than those obtained by VQE with a unitary coupled cluster ansatz. EVQE demonstrates significant noise-resistance properties, obtaining results in noisy simulation with at least $3.6\times$ less error than VQE using any tested ansatz configuration. We successfully evaluated EVQE on a real 5-qubit IBMQ quantum computer. The experimental results, which we obtained both via simulation and on real quantum hardware, demonstrate the effectiveness of EVQE for general-purpose optimization on the quantum computers of the present and near future.