标题： 基于单光子的通用量子计算平台 显示英文标题

标题： A general-purpose single-photon-based quantum computing platform

摘要： 量子计算旨在利用量子现象，高效地执行即使对于最强大的经典超级计算机来说也无法完成的计算。 在众多有前景的技术方法中，光子量子计算具有低退相干性、信息处理对低温要求较低以及与经典和量子网络原生集成的优势。 到目前为止，基于光的量子计算演示已经通过专用硬件实现了特定任务，特别是高斯玻色采样，这使得量子计算优势得以实现。 在此，我们报告了一个基于单光子的第一个用户可用的通用量子计算原型。 该设备包括一个高效率的量子点单光子源，为可重新配置芯片上的通用线性光学网络供能，其中硬件错误通过机器学习的转换过程进行补偿。 我们的完整软件栈允许远程控制该设备，通过逻辑门或直接光子操作执行计算。 对于基于门的计算，我们以最先进的保真度对一、二和三量子比特门进行了基准测试，分别为$99.6\pm0.1 \%$、$93.8\pm0.6 \%$和$86\pm1.2 \%$。 我们还实现了变分量子本征求解器，用于高精度计算氢分子的能量水平。 对于光子原生计算，我们使用基于$3$光子的量子神经网络实现了分类算法，并在通用可重新配置的集成电路上首次报告了$6$光子玻色采样演示。 最后，我们报告了首次预言的三光子纠缠生成，这是迈向基于测量的量子计算的关键里程碑。 显示英文摘要

摘要： Quantum computing aims at exploiting quantum phenomena to efficiently perform computations that are unfeasible even for the most powerful classical supercomputers. Among the promising technological approaches, photonic quantum computing offers the advantages of low decoherence, information processing with modest cryogenic requirements, and native integration with classical and quantum networks. To date, quantum computing demonstrations with light have implemented specific tasks with specialized hardware, notably Gaussian Boson Sampling which permitted quantum computational advantage to be reached. Here we report a first user-ready general-purpose quantum computing prototype based on single photons. The device comprises a high-efficiency quantum-dot single-photon source feeding a universal linear optical network on a reconfigurable chip for which hardware errors are compensated by a machine-learned transpilation process. Our full software stack allows remote control of the device to perform computations via logic gates or direct photonic operations. For gate-based computation we benchmark one-, two- and three-qubit gates with state-of-the art fidelities of $99.6\pm0.1 \%$, $93.8\pm0.6 \%$ and $86\pm1.2 \%$ respectively. We also implement a variational quantum eigensolver, which we use to calculate the energy levels of the hydrogen molecule with high accuracy. For photon native computation, we implement a classifier algorithm using a $3$-photon-based quantum neural network and report a first $6$-photon Boson Sampling demonstration on a universal reconfigurable integrated circuit. Finally, we report on a first heralded 3-photon entanglement generation, a key milestone toward measurement-based quantum computing.