论文标题
适用于特征值估计的受控门网络
Controlled Gate Networks Applied to Eigenvalue Estimation
论文作者
论文摘要
我们引入了一种新的量子电路设计方案，称为受控门网络。新策略不是试图减少单个单一操作的复杂性，而是要在最少数量的大门所需的所有统一操作之间切换。我们使用两个示例来说明我们的方法。第一个示例是两个Quibent系统的各种子空间计算。我们证明了计算内部产物和哈密顿矩阵元素所需的两倍门的数量大约减少了五倍。第二个示例是使用牛仔竞技算法使用特定类别的受控门网络来估算两数Qubithiphtonian的特征值，称为受控逆转门。同样，证明了两倍大门的数量减少了五倍。我们使用Quantinuum H1-2和IBM珀斯设备来实现量子电路。我们的工作表明，受控的门网络是降低量子算法中量子多体问题的栅极复杂性的有用工具。
We introduce a new scheme for quantum circuit design called controlled gate networks. Rather than trying to reduce the complexity of individual unitary operations, the new strategy is to toggle between all of the unitary operations needed with the fewest number of gates. We illustrate our approach using two examples. The first example is a variational subspace calculation for a two-qubit system. We demonstrate an approximately five-fold reduction in the number of two-qubit gates required for computing inner products and Hamiltonian matrix elements. The second example is estimating the eigenvalues of a two-qubit Hamiltonian via the Rodeo Algorithm using a specific class of controlled gate networks called controlled reversal gates. Again, a fivefold reduction in the number of two-qubit gates is demonstrated. We use the Quantinuum H1-2 and IBM Perth devices to realize the quantum circuits. Our work demonstrates that controlled gate networks are a useful tool for reducing gate complexity in quantum algorithms for quantum many-body problems.
