论文标题
多原子分子的势能表面的量子高斯工艺模型
Quantum Gaussian process model of potential energy surface for a polyatomic molecule
论文作者
论文摘要
借助量子计算机的门，旨在编码多维向量的量子计算机，量子计算机状态在特定的量子状态状态上的投影可以产生复制内核Hilbert Space的内核。我们表明，可以在当前量子计算机上实现的固定ANSATZ获得的量子内核，可用于用于多原子分子的全球势能表面（PES）的准确回归模型。为了获得准确的回归模型，我们通过改变量子门的参数来应用贝叶斯优化以最大程度地提高边际可能性。这产生具有量子内核的高斯工艺模型。我们说明了量子纠缠在量子内核中的影响，并通过推断能量域中的全局六维PE来探索量子高斯过程的概括性能。
With gates of a quantum computer designed to encode multi-dimensional vectors, projections of quantum computer states onto specific qubit states can produce kernels of reproducing kernel Hilbert spaces. We show that quantum kernels obtained with a fixed ansatz implementable on current quantum computers can be used for accurate regression models of global potential energy surfaces (PES) for polyatomic molecules. To obtain accurate regression models, we apply Bayesian optimization to maximize marginal likelihood by varying the parameters of the quantum gates. This yields Gaussian process models with quantum kernels. We illustrate the effect of qubit entanglement in the quantum kernels and explore the generalization performance of quantum Gaussian processes by extrapolating global six-dimensional PES in the energy domain.
