Continuous-variable quantum approximate optimization on a programmable photonic quantum processor
Continuous-variable quantum approximate optimization on a programmable photonic quantum processor
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DOI：
10.1103/physrevresearch.5.043005
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发表时间：
2022-06
影响因子：
4.2
通讯作者：
Y. Enomoto;K. Anai;K. Udagawa;S. Takeda
中科院分区：
文献类型：
--
作者：
Y. Enomoto;K. Anai;K. Udagawa;S. Takeda
变分量子算法（VQA）提供了一种很有前途的方法来实现量子优势的实际问题的短期噪声的中间尺度量子（NISQ）设备。到目前为止，大多数关于VQA的研究都集中在基于量子比特的系统上，但VQA的能力可以通过利用无限维连续变量（CV）系统来提高。在这里，我们实现了CV版本的一个VQA，量子近似优化算法，通过开发一个可编程光子量子计算机和经典计算机之间的自动协作计算系统。我们的实验表明，该算法解决了简单的连续函数的最小化问题，通过实施量子版本的梯度下降本地化最初广泛分布的波函数的最小值。该方法允许在物理平台上执行实用的CV量子算法。我们的工作可以扩展到更一般的函数的最小化，提供了一种替代方案，以实现在实际问题中的量子优势。
Variational quantum algorithms (VQAs) provide a promising approach to achieving quantum advantage for practical problems on near-term noisy intermediate-scale quantum (NISQ) devices. Thus far, most studies on VQAs have focused on qubit-based systems, but the power of VQAs can be potentially boosted by exploiting infinite-dimensional continuous-variable (CV) systems. Here, we implement the CV version of one VQA, a quantum approximate optimization algorithm by developing an automated collaborative computing system between a programmable photonic quantum computer and a classical computer. We experimentally demonstrate that this algorithm solves the minimization problem of simple continuous functions by implementing the quantum version of gradient descent to localize an initially broadly-distributed wavefunction to the minimum. This method allows the execution of a practical CV quantum algorithm on a physical platform. Our work can be extended to the minimization of more general functions, providing an alternative to achieve the quantum advantage in practical problems.