Moderate-size quantum computers are now publicly accessible over the cloud, opening the exciting possibility of performing dynamical simulations of quantum systems. However, while rapidly improving, these devices have short coherence times, limiting the depth of algorithms that may be successfully implemented. Here we demonstrate that, despite these limitations, it is possible to implement long-time, high fidelity simulations on current hardware. Specifically, we simulate an XY-model spin chain on the Rigetti and IBM quantum computers, maintaining a fidelity of at least 0.9 for over 600 time steps. This is a factor of 150 longer than is possible using the iterated Trotter method. Our simulations are performed using a new algorithm that we call the fixed state Variational Fast Forwarding (fsVFF) algorithm. This algorithm decreases the circuit depth and width required for a quantum simulation by finding an approximate diagonalization of a short time evolution unitary. Crucially, fsVFF only requires finding a diagonalization on the subspace spanned by the initial state, rather than on the total Hilbert space as with previous methods, substantially reducing the required resources. We further demonstrate the viability of fsVFF through large numerical implementations of the algorithm, as well as an analysis of its noise resilience and the scaling of simulation errors.
翻译:中度量子计算机现在可以公开进入云层,打开对量子系统进行动态模拟的令人振奋的可能性。 然而,虽然快速改进,这些装置具有短暂的一致性时间,限制了可能成功执行的算法深度。 我们在这里证明,尽管存在这些限制,但仍有可能对当前硬件进行长期、高度忠诚的模拟。 具体地说,我们在里贝蒂和IBM量子计算机上模拟一个XY模型的旋转链,在600多个步骤上保持至少0.9的忠诚度。 这是一个比使用迭接Trotter方法的可能性长150倍的系数。 我们的模拟是使用一种新算法进行的,我们称之为固定的快速前进变动算法(fVFF)算法。这种算法通过找到对短时间进化统一体的大致分级化,降低了量子模拟所需的深度和宽度。 克鲁西, FsVFF只需要在初始状态所覆盖的子空间上找到一个对等分级化度,而不是在使用迭接器的总空间上比可能要长150倍。 我们的模拟是使用一种新的算法,我们称之为固定的固定的快速快速前进算法,大大降低了所需的资源。 我们进一步展示了FFF的弹性分析,进一步展示了它的弹性分析, 。