Dai J, Krems R V
Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1, CanadaStewart Blusson Quantum Matter Institute, Vancouver, British Columbia V6T 1Z4, Canada.
J Chem Phys. 2022 May 14;156(18):184802. doi: 10.1063/5.0088821.
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 (PESs) 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 PESs in the energy domain.
通过设计用于编码多维向量的量子计算机门，量子计算机状态在特定量子比特状态上的投影可以产生再生核希尔伯特空间的核。我们表明，使用当前量子计算机上可实现的固定量子态制备方法获得的量子核可用于多原子分子全局势能面（PES）的精确回归模型。为了获得精确的回归模型，我们应用贝叶斯优化，通过改变量子门的参数来最大化边际似然。这产生了具有量子核的高斯过程模型。我们说明了量子核中量子比特纠缠的影响，并通过在能量域中外推全局六维PES来探索量子高斯过程的泛化性能。
