论文标题
可编程光子量子处理器上的连续变量量子近似优化
Continuous-variable quantum approximate optimization on a programmable photonic quantum processor
论文作者
论文摘要
变异量子算法（VQA）为在近期噪声中间尺度量子（NISQ）设备上实现量子优势提供了一种有希望的方法。到目前为止，大多数关于VQA的研究都集中在基于Qubit的系统上，但是VQA的功能可以通过利用无限二维连续变量（CV）系统来增强。在这里，我们通过在可编程的光子量子计算机和经典计算机之间开发自动化的协作计算系统，实现了一个VQA的简历版本，这是一种量子近似优化算法。我们通过实验表明，该算法通过实现梯度下降的量子版本来解决最小值的最小连续功能的最小化问题，以将最初分布的波函数定位到最小值。此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最小化功能的最小化，为实现实际问题的量子优势提供了替代方案。
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.
