论文标题
关于使用嘈杂的中级量表量子计算机的量子保理
On quantum factoring using noisy intermediate scale quantum computers
论文作者
论文摘要
我们研究了不同实例大小和优化算法的变异量子保理（VQF）算法的性能和资源用途。我们的模拟在使用VQE而不是QAOA进行优化时显示出更好的机会。在基于梯度的优化中，我们发现量子电路梯度估计所需的时间是一个重要的问题，如果VQF与经典保理算法竞争。此外，我们比较了VQE优化中的纠缠和非输入电路，并且未能看到大量证据支持将纠缠在VQE电路中。
We study the performance and resource usage of the variational quantum factoring (VQF) algorithm for different instance sizes and optimization algorithms. Our simulations show better chance of finding the ground state when using VQE rather than QAOA for optimization. In gradient-based optimization we find that the time required for quantum circuit gradient estimation is a significant problem if VQF is to become competitive with classical factoring algorithms. Further, we compare entangled and non-entangled circuits in VQE optimization and fail to see significant evidence in favour of including entanglement in the VQE circuit.
