论文标题

方程线性系统的大规模量子混合溶液

Large-scale quantum hybrid solution for linear systems of equations

论文作者

Perelshtein, M. R., Pakhomchik, A. I., Melnikov, A. A., Novikov, A. A., Glatz, A., Paraoanu, G. S., Vinokur, V. M., Lesovik, G. B.

论文摘要

最先进的嘈杂的中间规模量子设备(NISQ),尽管不完美,但可以实现显然超出现代经典超级计算机功能的计算任务。但是,当前的量子计算仅限于探索特定的简化协议,而实施全尺寸量子算法旨在解决数据分析和数值建模中引起的具体大规模问题仍然是一个挑战。在这里,我们介绍并实施了一种混合量子算法,用于求解具有指数加速的方程式的线性系统,利用量子相估计,这是用于量子计算的典范核心协议之一。我们介绍了适用于当前一代量子机的线性系统的理论类别类别,并在实验上解决了超导IBMQ设备上的$ 2^{17} $ - 尺寸问题,这是量子计算机上线性系统解决方案的记录。所考虑的大规模算法比常规解决方案表现出优势,通过阶段估计证明了量子数据处理的优势,并且对满足实际相关挑战的有很高的希望。

State-of-the-art noisy intermediate-scale quantum devices (NISQ), although imperfect, enable computational tasks that are manifestly beyond the capabilities of modern classical supercomputers. However, present quantum computations are restricted to exploring specific simplified protocols, whereas the implementation of full-scale quantum algorithms aimed at solving concrete large scale problems arising in data analysis and numerical modelling remains a challenge. Here we introduce and implement a hybrid quantum algorithm for solving linear systems of equations with exponential speedup, utilizing quantum phase estimation, one of the exemplary core protocols for quantum computing. We introduce theoretically classes of linear systems that are suitable for current generation quantum machines and solve experimentally a $2^{17}$-dimensional problem on superconducting IBMQ devices, a record for linear system solution on quantum computers. The considered large-scale algorithm shows superiority over conventional solutions, demonstrates advantages of quantum data processing via phase estimation and holds high promise for meeting practically relevant challenges.

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