Recent advances in quantum computing systems attract tremendous attention. Commercial companies, such as IBM, Amazon, and IonQ, have started to provide access to noisy intermediate-scale quantum computers. Researchers and entrepreneurs attempt to deploy their applications that aim to achieve a quantum speedup. Grover's algorithm and quantum phase estimation are the foundations of many applications with the potential for such a speedup. While these algorithms, in theory, obtain marvelous performance, deploying them on existing quantum devices is a challenging task. For example, quantum phase estimation requires extra qubits and a large number of controlled operations, which are impractical due to low-qubit and noisy hardware. To fully utilize the limited onboard qubits, we propose IQuCS, which aims at index searching and counting in a quantum-classical hybrid system. IQuCS is based on Grover's algorithm. From the problem size perspective, it analyzes results and tries to filter out unlikely data points iteratively. A reduced data set is fed to the quantum computer in the next iteration. With a reduction in the problem size, IQuCS requires fewer qubits iteratively, which provides the potential for a shared computing environment. We implement IQuCS with Qiskit and conduct intensive experiments. The results demonstrate that it reduces qubits consumption by up to 66.2%.
翻译:量子计算系统最近的进展引来巨大的关注。 IBM、亚马逊和IonQ等商业公司已开始提供使用噪音的中间级量子计算机的机会。 研究人员和企业家试图部署其应用程序,以实现量子加速。 Grover的算法和量子阶段估算是许多应用的基础,这些应用有可能加快速度。 虽然这些算法在理论上取得了卓越的性能,但在现有的量子装置上部署它们是一项具有挑战性的任务。例如,量子阶段估算需要额外的qubit和大量的受控操作,由于低Qbit和噪音的硬件,这些操作不切实际。要充分利用板上的有限量子计算机,我们建议IQuCS, 目的是在量子级混合系统中进行指数搜索和计算。 Grover的算法以Grover的算法为基础。从问题大小的角度分析结果,并尝试用迭接方式将不可能的数据点过滤出来。 减少的数据集被输入到下个量子计算机。随着问题规模的缩小, IQCS要求减少对量子基比值的量位值, 并用大量的计算结果。 降低我们对QQ的计算结果。 降低了CSqbitbitbit 。