论文标题
张量 - 网络辅助变分量子算法
Tensor-network-assisted variational quantum algorithm
论文作者
论文摘要
近期的量子设备通常会遭受浅层电路深度的影响，因此由于噪声和脱干而导致的表达有限。为了解决这个问题，我们提出了张量 - 网络辅助参数化量子电路，该电路将经典的张量 - 网络运算符与量子电路相连，以有效地提高电路的表现性，而无需物理更深的电路。我们提出了一个张量 - 网络辅助的变分量子算法的框架，该算法可以使用较浅的量子电路解决量子多体问题。我们通过考虑两个统一基质 - 产品运营商和单一树张量网络的示例来证明这种方法的效率，这表明它们都可以有效地实施。通过数值模拟，我们表明这些电路的表达能力在张量网络的帮助下大大提高了。我们将我们的方法应用于二维ISING模型和具有多达16个量子位的一维时晶模型模型，并证明我们的方法始终使用浅量子电路优于常规方法。
Near-term quantum devices generally suffer from shallow circuit depth and hence limited expressivity due to noise and decoherence. To address this, we propose tensor-network-assisted parametrized quantum circuits, which concatenate a classical tensor-network operator with a quantum circuit to effectively increase the circuit's expressivity without requiring a physically deeper circuit. We present a framework for tensor-network-assisted variational quantum algorithms that can solve quantum many-body problems using shallower quantum circuits. We demonstrate the efficiency of this approach by considering two examples of unitary matrix-product operators and unitary tree tensor networks, showing that they can both be implemented efficiently. Through numerical simulations, we show that the expressivity of these circuits is greatly enhanced with the assistance of tensor networks. We apply our method to two-dimensional Ising models and one-dimensional time-crystal Hamiltonian models with up to 16 qubits and demonstrate that our approach consistently outperforms conventional methods using shallow quantum circuits.
