论文标题
用于Grover搜索的量子多编程
Quantum multi-programming for Grover's search
论文作者
论文摘要
量子多编程是一种通过同时执行多个量子电路来利用当代噪声中间量子计算机的方法。尽管对此进行了早期研究，但该研究仍在量子门或小型量子算法上无关。在本文中，我们提出了一种用于Grover搜索的量子多编程（QMP）算法。我们的算法通过部分扩散操作员分解了Grover的算法，并通过QMP并行执行分解电路。我们证明了这种新算法增加了Grover操作员的旋转角度，从而增加了成功概率。新算法是在IBM量子计算机上实现的，并将其与Grover Grover的算法和Grover算法的其他变体进行了比较。经验测试验证了我们的新算法优于Grover算法以及规范Grover的算法的其他变化。
Quantum multi-programming is a method utilizing contemporary noisy intermediate-scale quantum computers by executing multiple quantum circuits concurrently. Despite early research on it, the research remains on quantum gates or small-size quantum algorithms without correlation. In this paper, we propose a quantum multi-programming (QMP) algorithm for Grover's search. Our algorithm decomposes Grover's algorithm by the partial diffusion operator and executes the decomposed circuits in parallel by QMP. We proved that this new algorithm increases the rotation angle of the Grover operator which, as a result, increases the success probability. The new algorithm is implemented on IBM quantum computers and compared with the canonical Grover's algorithm and other variations of Grover's algorithms. The empirical tests validate that our new algorithm outperforms other variations of Grover's algorithms as well as the canonical Grover's algorithm.
